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Journal articles on the topic 'Computer Assisted Diagnosis (CAD)'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Chambara, Nonhlanhla, Shirley Yuk Wah Liu, Xina Lo, and Michael Ying. "Comparative Analysis of Computer-Aided Diagnosis and Computer-Assisted Subjective Assessment in Thyroid Ultrasound." Life 11, no. 11 (2021): 1148. http://dx.doi.org/10.3390/life11111148.

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The value of computer-aided diagnosis (CAD) and computer-assisted techniques equipped with different TIRADS remains ambiguous. Parallel diagnosis performances of computer-assisted subjective assessments and CAD were compared based on AACE, ATA, EU, and KSThR TIRADS. CAD software computed the diagnosis of 162 thyroid nodule sonograms. Two raters (R1 and R2) independently rated the sonographic features of the nodules using an online risk calculator while blinded to pathology results. Diagnostic efficiency measures were calculated based on the final pathology results. R1 had higher diagnostic per

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Caputo, Alessandro, Elisabetta Maffei, Nalini Gupta, et al. "Computer-assisted diagnosis to improve diagnostic pathology: A review." Indian Journal of Pathology and Microbiology 68, no. 1 (2025): 3–10. https://doi.org/10.4103/ijpm.ijpm_339_24.

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ABSTRACT With an increasing demand for accuracy and efficiency in diagnostic pathology, computer-assisted diagnosis (CAD) emerges as a prominent and transformative solution. This review aims to explore the practical applications, implications, strengths, and weaknesses of CAD applied to diagnostic pathology. A comprehensive literature search was conducted to include English-language studies focusing on CAD tools, digital pathology, and Artificial intelligence (AI) applications in pathology. The review underscores the transformative potential of CAD tools in pathology, particularly in streamlin

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Zhao, Wanjun, Qingbo Kang, Feiyan Qian, Kang Li, Jingqiang Zhu, and Buyun Ma. "Convolutional Neural Network-Based Computer-Assisted Diagnosis of Hashimoto’s Thyroiditis on Ultrasound." Journal of Clinical Endocrinology & Metabolism 107, no. 4 (2021): 953–63. http://dx.doi.org/10.1210/clinem/dgab870.

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Abstract Purpose This study investigates the efficiency of deep learning models in the automated diagnosis of Hashimoto’s thyroiditis (HT) using real-world ultrasound data from ultrasound examinations by computer-assisted diagnosis (CAD) with artificial intelligence. Methods We retrospectively collected ultrasound images from patients with and without HT from 2 hospitals in China between September 2008 and February 2018. Images were divided into a training set (80%) and a validation set (20%). We ensembled 9 convolutional neural networks (CNNs) as the final model (CAD-HT) for HT classification

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Dhull, Anuradha, Kavita Khanna, Akansha Singh, and Gaurav Gupta. "ACO Inspired Computer-aided Detection/Diagnosis (CADe/CADx) Model for Medical Data Classification." Recent Patents on Computer Science 12, no. 4 (2019): 250–59. http://dx.doi.org/10.2174/2213275912666181205155018.

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Background: Computer-Assisted Diagnosis (CAD) has become a common practice of use in the healthcare industry due to its improved accuracy and reliability. The CAD systems are expected to improve the quality of medical care by assisting healthcare professionals with a wide range of clinical decisions. A CAD system is a combination of Computer-Assisted Detection (CADe) and Computer-Assisted Diagnosis (CADx) system. Objective: The objective of this research article is to generate an optimized rule-set for medical diagnosis capable of providing improved accuracy. It is evident from the literature

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Giannini, Valentina, Simone Mazzetti, Giovanni Cappello, et al. "Computer-Aided Diagnosis Improves the Detection of Clinically Significant Prostate Cancer on Multiparametric-MRI: A Multi-Observer Performance Study Involving Inexperienced Readers." Diagnostics 11, no. 6 (2021): 973. http://dx.doi.org/10.3390/diagnostics11060973.

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Recently, Computer Aided Diagnosis (CAD) systems have been proposed to help radiologists in detecting and characterizing Prostate Cancer (PCa). However, few studies evaluated the performances of these systems in a clinical setting, especially when used by non-experienced readers. The main aim of this study is to assess the diagnostic performance of non-experienced readers when reporting assisted by the likelihood map generated by a CAD system, and to compare the results with the unassisted interpretation. Three resident radiologists were asked to review multiparametric-MRI of patients with and

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Kalejahi, Behnam Kiani, Saeed Meshgini, Sebelan Danishvar, and Sara Khorram. "Diagnosis of liver disease by computer- assisted imaging techniques: A literature review." Intelligent Data Analysis 26, no. 4 (2022): 1097–114. http://dx.doi.org/10.3233/ida-216379.

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Diagnosis of liver disease using computer-aided detection (CAD) systems is one of the most efficient and cost-effective methods of medical image diagnosis. Accurate disease detection by using ultrasound images or other medical imaging modalities depends on the physician’s or doctor’s experience and skill. CAD systems have a critical role in helping experts make accurate and right-sized assessments. There are different types of CAD systems for diagnosing different diseases, and one of the applications is in liver disease diagnosis and detection by using intelligent algorithms to detect any abno

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Kage, A., T. Wittenberg, S. Mühldorfer, and C. Münzenmayer. "Computer-assisted Diagnosis for Precancerous Lesions in the Esophagus." Methods of Information in Medicine 48, no. 04 (2009): 324–30. http://dx.doi.org/10.3414/me9230.

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Summary Objectives: The interpretation of endoscopic findings by gastroenterologists is still a difficult and highly subjective task. Despite important developments such as chromo-endoscopy, pit pattern analysis, fluorescence imaging as well as narrow band imaging it still requires lots of experience and training with a certain tentativeness until the final biopsy. By the development of computer-assisted diagnosis (CAD) systems this process can be supported. Methods: This paper presents a new approach to CAD for precancerous lesions in the esophagus based on color-texture analysis in a content

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Ochiai, Kentaro, Tsuyoshi Ozawa, Junichi Shibata, Soichiro Ishihara, and Tomohiro Tada. "Current Status of Artificial Intelligence-Based Computer-Assisted Diagnosis Systems for Gastric Cancer in Endoscopy." Diagnostics 12, no. 12 (2022): 3153. http://dx.doi.org/10.3390/diagnostics12123153.

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Artificial intelligence (AI) is gradually being utilized in various fields as its performance has been improving with the development of deep learning methods, availability of big data, and the progression of computer processing units. In the field of medicine, AI is mainly implemented in image recognition, such as in radiographic and pathologic diagnoses. In the realm of gastrointestinal endoscopy, although AI-based computer-assisted detection/diagnosis (CAD) systems have been applied in some areas, such as colorectal polyp detection and diagnosis, so far, their implementation in real-world c

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Benammar Elgaaied, Amel, Donato Cascio, Salvatore Bruno, et al. "Computer-Assisted Classification Patterns in Autoimmune Diagnostics: The AIDA Project." BioMed Research International 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/2073076.

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Antinuclear antibodies (ANAs) are significant biomarkers in the diagnosis of autoimmune diseases in humans, done by mean of Indirect ImmunoFluorescence (IIF) method, and performed by analyzing patterns and fluorescence intensity. This paper introduces the AIDA Project (autoimmunity: diagnosis assisted by computer) developed in the framework of an Italy-Tunisia cross-border cooperation and its preliminary results. A database of interpreted IIF images is being collected through the exchange of images and double reporting and a Gold Standard database, containing around 1000 double reported images

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Lee, Si Eun, and Eun-Kyung Kim. "Applications of artificial intelligence-based computer-assisted diagnosis in breast radiology: a narrative review." Journal of the Korean Medical Association 68, no. 5 (2025): 281–87. https://doi.org/10.5124/jkma.25.0045.

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Purpose: Mammography is the standard screening method for breast cancer, proven to reduce mortality. However, its diagnostic performance varies depending on patient characteristics and radiologist expertise. Dense breast tissue, present in approximately 70% of Korean women aged 40 to 59, limits detection by obscuring malignancies. Additionally, optimal interpretation requires extensive training, which is not always achievable. Artificial intelligence-based computer-aided diagnosis (AI-CAD) has emerged as a promising tool for enhancing mammographic accuracy and efficiency.Current Concepts: AI-C

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Jakkanwar, Mayuri S., and Dr A. W. Kiwelekar. "Brain Mapping Disease Identification System." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (2023): 1023–26. http://dx.doi.org/10.22214/ijraset.2023.49580.

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Abstract: This paper introduces concepts and principles of how computer assisted diagnosis (CAD) systems work and addresses the challenges of processing large amounts of medical data for diagnosis. It also provides an overview of CAD methods developed in the field of diagnosing neurological diseases and which existing technologies provide what big data for analyzing which diseases. Although there is extensive work on the development of CAD systems for automated screening of diseases, experts still cannot use them regularly in their decision-making processes due to the lack of easily available

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Zhu, Tianhao, Kexin Xu, Wonchan Son, et al. "Designing a computer-assisted diagnosis system for cardiomegaly detection and radiology report generation." PLOS Digital Health 4, no. 5 (2025): e0000835. https://doi.org/10.1371/journal.pdig.0000835.

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Chest X-ray (CXR) is a diagnostic tool for cardiothoracic assessment. They make up 50% of all diagnostic imaging tests. With hundreds of images examined every day, radiologists can suffer from fatigue. This fatigue may reduce diagnostic accuracy and slow down report generation. We describe a prototype computer-assisted diagnosis (CAD) pipeline employing computer vision (CV) and Natural Language Processing (NLP). It was trained and evaluated on the publicly available MIMIC-CXR dataset. We perform image quality assessment, view labelling, and segmentation-based cardiomegaly severity classificati

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Kaushal, C., S. Bhat, D. Koundal, and A. Singla. "Recent Trends in Computer Assisted Diagnosis (CAD) System for Breast Cancer Diagnosis Using Histopathological Images." IRBM 40, no. 4 (2019): 211–27. http://dx.doi.org/10.1016/j.irbm.2019.06.001.

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Thurfjell, E., M. Gelig Thurfjell, E. Egge, and N. Bjurstam. "Sensitivity and specificity of computer-assisted breast cancer detection in mammography screening." Acta Radiologica 39, no. 4 (1998): 384–88. http://dx.doi.org/10.1080/02841859809172450.

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Purpose: To evaluate a system of computer-assisted diagnosis (CAD) in mammography. Material and Methods: A sample of 120 sets of two-view mammograms was examined by an expert screener, a screening radiologist, a clinical radiologist, and a CAD system. The screening and clinical radiologists examined the mammograms twice, first without and then with the help of CAD. The sample consisted of first-round screening films from a two-round population-based screening, and comprised: 32 women in whom breast cancer was detected at the first screening; 10 with cancer detected during the screening interva

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Wibawa, Made Satria. "STUDI KOMPARASI METODE SEGMENTASI PARU-PARU PADA CITRA CT-SCAN AKSIAL." Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) 7, no. 3 (2019): 283. http://dx.doi.org/10.23887/janapati.v7i3.15751.

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Kanker paru dapat diobati jika diagnosis dini dilakukan. Diagnosis dapat dilakukan menggunakan modalitas citra Computed Tomography (CT). Diagnosis kanker paru melalui citra CT dilakukan oleh tenaga medis. Untuk membantu diagnosis kanker, tenaga medis dapat dibantu dengan Computer Assisted Diagnosis (CAD). Dalam CAD, tahapan pertama yang paling penting adalah segmentasi citra paru-paru. Penelitian ini melakukan studi komparasi metode segmentasi citra CT paru-paru. Terdapat tiga metode segmentasi yang digunakan, yaitu Otsu, K-Means dan Fuzzy C-Means. Proses evaluasi menggunakan metrik akurasi, t

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Maharjan, Shyam K., S. R. B. Mathema, and S. M. Shrestha. "3D Guided Implant Surgery: A Case Report." Journal of Nepalese Prosthodontic Society 1, no. 2 (2018): 90–95. http://dx.doi.org/10.3126/jnprossoc.v1i2.23863.

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Dental implant has been established as one of the most predictable and conservative approaches for the rehabilitation of missing teeth. With the evolution of newer material and advancement in digital technology, specially in computed tomography, implant planning software and guided implant surgery, the outcome and success in implant dentistry has become more predictable. Computer aided design (CAD) technique is being integrated into treatment planning, and computer-assisted manufacturing (CAM) is becoming more popular for rehabilitation procedures. This case report illustrate the diagnosis, pl

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Zhao, Zihao, Sheng Wang, Qian Wang, and Dinggang Shen. "Mining Gaze for Contrastive Learning toward Computer-Assisted Diagnosis." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 7 (2024): 7543–51. http://dx.doi.org/10.1609/aaai.v38i7.28586.

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Obtaining large-scale radiology reports can be difficult for medical images due to ethical concerns, limiting the effectiveness of contrastive pre-training in the medical image domain and underscoring the need for alternative methods. In this paper, we propose eye-tracking as an alternative to text reports, as it allows for the passive collection of gaze signals without ethical issues. By tracking the gaze of radiologists as they read and diagnose medical images, we can understand their visual attention and clinical reasoning. When a radiologist has similar gazes for two medical images, it may

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Shehata, Mohamed, Ahmed Alksas, Rasha T. Abouelkheir, et al. "A Comprehensive Computer-Assisted Diagnosis System for Early Assessment of Renal Cancer Tumors." Sensors 21, no. 14 (2021): 4928. http://dx.doi.org/10.3390/s21144928.

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Renal cell carcinoma (RCC) is the most common and a highly aggressive type of malignant renal tumor. In this manuscript, we aim to identify and integrate the optimal discriminating morphological, textural, and functional features that best describe the malignancy status of a given renal tumor. The integrated discriminating features may lead to the development of a novel comprehensive renal cancer computer-assisted diagnosis (RC-CAD) system with the ability to discriminate between benign and malignant renal tumors and specify the malignancy subtypes for optimal medical management. Informed cons

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SOOD, SURABHI, MOHIT KUMAR, RAM BILAS PACHORI, and U. RAJENDRA ACHARYA. "APPLICATION OF EMPIRICAL MODE DECOMPOSITION–BASED FEATURES FOR ANALYSIS OF NORMAL AND CAD HEART RATE SIGNALS." Journal of Mechanics in Medicine and Biology 16, no. 01 (2016): 1640002. http://dx.doi.org/10.1142/s0219519416400029.

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Coronary Artery Disease (CAD) is a heart disease caused due to insufficient supply of nutrients and oxygen to the heart muscles. Hence, reduced supply of nutrients and oxygen causes heart attack or stroke and may cause death. Also significant number of people are suffering from CAD around the world so timely diagnosis of CAD can save the life of patients. In this work, we have proposed computer assisted diagnosis of CAD using Heart Rate (HR) signals obtained from Electrocardiogram (ECG) signals. We have used the Empirical Mode Decomposition (EMD) technique to process the HR signals. The featur

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Akella, S. Narasimha Raju, Jayavel Kayalvizhi, and Rajalakshmi Tulasi. "Intelligent recognition of colorectal cancer combining application of computer-assisted diagnosis with deep learning approaches." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 1 (2022): 738–47. https://doi.org/10.11591/ijece.v12i1.pp738-747.

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The malignancy of the colorectal testing methods has been exposed triumph to decrease the occurrence and death rate; this cancer is the relatively sluggish rising and has an extremely peculiar to develop the premalignant lesions. Now, many patients are not going to colorectal cancer screening, and people who do, are able to diagnose existing tests and screening methods. The most important concept of this motivation for this research idea is to evaluate the recognized data from the immediately available colorectal cancer screening methods. The data provided to laboratory technologists is import

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Qureshi, Imran, Jun Ma, and Qaisar Abbas. "Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy." Symmetry 11, no. 6 (2019): 749. http://dx.doi.org/10.3390/sym11060749.

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Diabetic retinopathy (DR) is a complication of diabetes that exists throughout the world. DR occurs due to a high ratio of glucose in the blood, which causes alterations in the retinal microvasculature. Without preemptive symptoms of DR, it leads to complete vision loss. However, early screening through computer-assisted diagnosis (CAD) tools and proper treatment have the ability to control the prevalence of DR. Manual inspection of morphological changes in retinal anatomic parts are tedious and challenging tasks. Therefore, many CAD systems were developed in the past to assist ophthalmologist

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A, Jenifer Sofia, and A. Ruhan Bevi. "A Review on Artificial Intelligence – Assisted CCTA Imaging for CAD Diagnosis." Computational Intelligence and Machine Learning 3, no. 1 (2020): 26–30. http://dx.doi.org/10.36647/ciml/03.01.a004.

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According to the statistics committee of the American Heart Association, Coronary Artery Disease (CAD) or myocardial ischemia is one of the most common Cardiovascular Diseases (CVD) that has high morbidity and mortality worldwide. Though Invasive Coronary Angiography (ICA) is recognized as the gold standard for the diagnosis of stenosis-related CAD owing to its ability to identify and classify stenoses precisely, it has severe complications and side effects. As a result, Image segmentation evaluation parameters and Automatic diagnosis have all benefited by using AI in non invasive technology k

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Moghbel, Mehrdad, and Syamsiah Mashohor. "A review of computer assisted detection/diagnosis (CAD) in breast thermography for breast cancer detection." Artificial Intelligence Review 39, no. 4 (2011): 305–13. http://dx.doi.org/10.1007/s10462-011-9274-2.

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Moga, Tudor Voicu, Alina Popescu, Ioan Sporea, et al. "Is Contrast Enhanced Ultrasonography a useful tool in a beginner’s hand? How much can a Computer Assisted Diagnosis prototype help in characterizing the malignancy of focal liver lesions?" Medical Ultrasonography 19, no. 3 (2017): 252. http://dx.doi.org/10.11152/mu-936.

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Aim: Contrast enhanced ultrasound (CEUS) improved the characterization of focal liver lesions (FLLs), but is an operatordependent method. The goal of this paper was to test a computer assisted diagnosis (CAD) prototype and to see its benefit in assisting a beginner in the evaluation of FLLs.Material and method: Our cohort included 97 good quality CEUS videos[34% hepatocellular carcinomas (HCC), 12.3% hypervascular metastases (HiperM), 11.3% hypovascular metastases (HipoM), 24.7% hemangiomas (HMG), 17.5% focal nodular hyperplasia (FNH)] that were used to develop a CAD prototype based on an algo

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Narasimha Raju, Akella S., Kayalvizhi Jayavel, and Tulasi Rajalakshmi. "Intelligent recognition of colorectal cancer combining application of computer-assisted diagnosis with deep learning approaches." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 1 (2022): 738. http://dx.doi.org/10.11591/ijece.v12i1.pp738-747.

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<span>The malignancy of the colorectal testing methods has been exposed triumph to decrease the occurrence and death rate; this cancer is the relatively sluggish rising and has an extremely peculiar to develop the premalignant lesions. Now, many patients are not going to colorectal cancer screening, and people who do, are able to diagnose existing tests and screening methods. The most important concept of this motivation for this research idea is to evaluate the recognized data from the immediately available colorectal cancer screening methods. The data provided to laboratory technologis

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Tiwari, Shamik. "An Analysis in Tissue Classification for Colorectal Cancer Histology Using Convolution Neural Network and Colour Models." International Journal of Information System Modeling and Design 9, no. 4 (2018): 1–19. http://dx.doi.org/10.4018/ijismd.2018100101.

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Computer vision-based identification of different tissue categories in histological images is a critical application of the computer-assisted diagnosis (CAD). Computer-assisted diagnosis systems support to reduce the cost and increase the efficiency of this process. Traditional image classification approaches depend on feature extraction methods designed for a specific problem based on domain information. Deep learning approaches are becoming important alternatives with advance of machine learning technologies to overcome the numerous difficulties of the feature-based approaches. A method for

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Sammouda, Rachid, Abdu Gumaei, and Ali El-Zaart. "Intelligent Computer-Aided Prostate Cancer Diagnosis Systems: State-of-the-Art and Future Directions." Mathematical Problems in Engineering 2021 (July 7, 2021): 1–17. http://dx.doi.org/10.1155/2021/9955174.

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Prostate Cancer (PCa) is one of the common cancers among men in the world. About 16.67% of men will be affected by PCa in their life. Due to the integration of magnetic resonance imaging in the current clinical procedure for detecting prostate cancer and the apparent success of imaging techniques in the estimation of PCa volume in the gland, we provide a more detailed review of methodologies that use specific parameters for prostate tissue representation. After collecting over 200 researches on image-based systems for diagnosing prostate cancer, in this paper, we provide a detailed review of e

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M.Sucharitha, Sesha Talpa Sai PHV, L.R. Chaitanya Lahari M., and Haseena Bee P. "Computer Assisted System for Detecting Pulmonary Embolism in Lungs." International Journal of Recent Technology and Engineering (IJRTE) 10, no. 4 (2021): 89–94. https://doi.org/10.35940/ijrte.D6584.1110421.

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A pulmonary embolism (PE) occurs when a blood artery in the lungs becomes suddenly blocked, generally owing to a blood clot. PE is a frequent life-threatening illness that should be diagnosed as soon as possible. A novel approach for automatically detecting PE in contrast-enhanced CT images is suggested in this research. To identify PE, computerized tomography (CT) is the main test to capture images. It is quick test, incursive with good quality images, enhanced contrast and multi-sliced images can be obtained. Candidate identification, feature calculation, and classification are all part of t

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Sebastian, J., I. D. Olaru, A. Giannakis, et al. "Detection of other pathologies when utilising computer-assisted digital solutions for TB screening." IJTLD OPEN 1, no. 12 (2024): 533–39. https://doi.org/10.5588/ijtldopen.24.0428.

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<sec><title>BACKGROUND</title>Computer-aided detection (CAD) tools for TB detection have the potential to enable screening programmes and reduce the diagnostic gap in settings where access to radiologists is limited. However, there are concerns that other common chest X-ray (CXR) abnormalities not due to TB may be missed.</sec><sec><title>METHODS</title>We assessed the performance of three commercialised CAD tools (qXR, INSIGHT CXR and DrAIDTM TB XR) to detect common non-TB abnormalities against readings with a standardised annotation guide by an exper

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Yoon, Jung Hyun, Kyungwha Han, Hee Jung Suh, Ji Hyun Youk, Si Eun Lee, and Eun-Kyung Kim. "Artificial intelligence-based computer-assisted detection/diagnosis (AI-CAD) for screening mammography: Outcomes of AI-CAD in the mammographic interpretation workflow." European Journal of Radiology Open 11 (December 2023): 100509. http://dx.doi.org/10.1016/j.ejro.2023.100509.

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Gupta, Suneet, V. Saravanan, Amarendranath Choudhury, Abdullah Alqahtani, Mohamed R. Abonazel, and K. Suresh Babu. "Supervised Computer-Aided Diagnosis (CAD) Methods for Classifying Alzheimer’s Disease-Based Neurodegenerative Disorders." Computational and Mathematical Methods in Medicine 2022 (May 23, 2022): 1–10. http://dx.doi.org/10.1155/2022/9092289.

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Alzheimer’s disease is incurable at the moment. If it can be appropriately diagnosed, the correct treatment can postpone the patient’s illness. To aid in the diagnosis of Alzheimer’s disease and to minimize the time and expense associated with manual diagnosis, a machine learning technique is employed, and a transfer learning method based on 3D MRI data is proposed. Machine learning algorithms can dramatically reduce the time and effort required for human treatment of Alzheimer’s disease. This approach extracts bottleneck features from the M-Net migration network and then adds a top layer to s

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Abhijeet, Kapoor, Eesha Mody, Brintha Jei J., Peter John, Murugesan Krishnan, and Muthukumar B. "Mobile Phone Assisted 3D Extra Oral Scanner for Acquiring Dental Digital Models - An Innovative Approach." Journal of Evolution of Medical and Dental Sciences 10, no. 23 (2021): 1815–18. http://dx.doi.org/10.14260/jemds/2021/375.

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An accurate impression of the edentulous arch is of utmost importance in the rehabilitation of completely edentulous cases. The steps that follow the impression making are highly influenced by the accuracy of the impression. In order to overcome the manual inadequacies and to improve the accuracy, various advancements have been made manually as well as digitally. Computer-aided design/computer-aided manufacturing (CAD / CAM)is one such digital advancement, which primarily aims at scanning the edentulous arches, followed by subsequent designing of the dentures and processing through various met

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Ben, Rabeh Amira, Faouzi Benzarti, Hamid Amiri, and Ben Djebara Mouna. "BRAIN Journal - New Computer Assisted Diagnostic to Detect Alzheimer Disease." BRAIN - Broad Research in Artificial Intelligence and Neuroscience 7, no. 3 (2016): 75–93. https://doi.org/10.5281/zenodo.1044969.

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ABSTRACT We describe a new Computer Assisted Diagnosis (CAD) to automatically detect Alzheimer Patients (AD), Mild Cognitive Impairment (MCI) and elderly Controls, based on the segmentation and classification of the Hippocampus (H) and Corpus Calosum (CC) from Magnetic Resonance Images (MRI). For the segmentation we used a new method based on a deformable model to extract the area wishes, and then we computed the geometric and texture features. For the classification we proposed a new supervised method. We evaluated the accuracy of our method in a group of 25 patients with AD (age±standard-dev

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Nam, Soo Jeong, Yosep Chong, Chan Kwon Jung, et al. "Preference and Demand for Digital Pathology and Computer-Aided Diagnosis among Korean Pathologists: A Survey Study Focused on Prostate Needle Biopsy." Applied Sciences 11, no. 16 (2021): 7380. http://dx.doi.org/10.3390/app11167380.

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Digital pathology systems (DPSs) have been globally implemented, and computer-assisted diagnosis (CAD) software has been actively developed in recent years. This study aimed to investigate perceptions of digital pathology and the demand for CAD. An online survey involving members of the Korean Society of Pathologists was conducted, and a demonstration clip of the diagnostic assistant software for a prostate needle biopsy was shown to them to provide a simple experience with CAD. One hundred sixty-four Korean pathologists (13.6% of 1210 Korean pathologists) participated. The majority (77.4%) an

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Azif, Faza Maula, Hanung Adi Nugroho, and Sunu Wibirama. "Detection of malaria parasites in thick blood smear: A review." Communications in Science and Technology 3, no. 1 (2018): 27–35. http://dx.doi.org/10.21924/cst.3.1.2018.75.

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Based on data from World Health Organization, in 2015, there are 90% of deaths caused by malaria disease in Africa, Southeast Asia and countries of eastern Mediterranean. It makes the malaria become one of the most dangerous diseases that often leads to death. To support the diagnosis of malaria, early detection of plasmodium parasite is needed. Recently, malaria diagnosis process can be done with the help of computer, or often referred to as Computer Aided Diagnosis (CAD). By utilizing the digital image from the blood staining process, digital image processing can be performed to detect the p

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Gudigar, Anjan, Sneha Nayak, Jyothi Samanth, et al. "Recent Trends in Artificial Intelligence-Assisted Coronary Atherosclerotic Plaque Characterization." International Journal of Environmental Research and Public Health 18, no. 19 (2021): 10003. http://dx.doi.org/10.3390/ijerph181910003.

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Coronary artery disease is a major cause of morbidity and mortality worldwide. Its underlying histopathology is the atherosclerotic plaque, which comprises lipid, fibrous and—when chronic—calcium components. Intravascular ultrasound (IVUS) and intravascular optical coherence tomography (IVOCT) performed during invasive coronary angiography are reference standards for characterizing the atherosclerotic plaque. Fine image spatial resolution attainable with contemporary coronary computed tomographic angiography (CCTA) has enabled noninvasive plaque assessment, including identifying features assoc

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Sucharitha, Dr M., Dr P. H. V. Sesha Talpa Sai, Ms M. L. R. Chaitanya Lahari, and Ms P. Haseena Bee. "Computer Assisted System for Detecting Pulmonary Embolism in Lungs." International Journal of Recent Technology and Engineering (IJRTE) 10, no. 4 (2021): 89–94. http://dx.doi.org/10.35940/ijrte.d6584.1110421.

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A pulmonary embolism (PE) occurs when a blood artery in the lungs becomes suddenly blocked, generally owing to a blood clot. PE is a frequent life-threatening illness that should be diagnosed as soon as possible. A novel approach for automatically detecting PE in contrast-enhanced CT images is suggested in this research. To identify PE, computerized tomography (CT) is the main test to capture images. It is quick test, incursive with good quality images, enhanced contrast and multi-sliced images can be obtained. Candidate identification, feature calculation, and classification are all part of t

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Singh, Manisha, Gurubasavaraj Veeranna Pujar, Sethu Arun Kumar, et al. "Evolution of Machine Learning in Tuberculosis Diagnosis: A Review of Deep Learning-Based Medical Applications." Electronics 11, no. 17 (2022): 2634. http://dx.doi.org/10.3390/electronics11172634.

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Tuberculosis (TB) is an infectious disease that has been a major menace to human health globally, causing millions of deaths yearly. Well-timed diagnosis and treatment are an arch to full recovery of the patient. Computer-aided diagnosis (CAD) has been a hopeful choice for TB diagnosis. Many CAD approaches using machine learning have been applied for TB diagnosis, specific to the artificial intelligence (AI) domain, which has led to the resurgence of AI in the medical field. Deep learning (DL), a major branch of AI, provides bigger room for diagnosing deadly TB disease. This review is focused

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Bratinčević, Lucija, and Tatjana Matijaš. "Application of CAD in the diagnosis of breast cancer." Radiološki vjesnik 46, no. 1 (2022): 2–11. http://dx.doi.org/10.55378/rv.46.1.1.

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Breast cancer is an extremely dangerous disease, which if diagnosed in time has a high survival rate. The incidence and mortality rate from breast malignancies are increasing, so in order to reduce these numbers, new technological solutions are being looked into, that should enable the earliest possible detection of breast carcinoma. Although the original solution was seen in traditional computer-assisted detection systems, CAD, applied to various radiological breast imaging methods, the results of various studies discussed in this paper found that they did not meet their original expectations

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Deepa, P., M. Arulselvi, and S. Meenakshi Sundaram. "Classification of Lung Cancer using Pre-Trained Deep Learning Models." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 09 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem37530.

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Many Computer Assisted Diagnosis (CAD) systems have been designed and used in recent past for diagnosingdifferenttypes of cancer. Identification of carcinoma at an earlier stage is more important, and it is made possible due to the use modern image processing and deep learning methods.The occurrence of Lung cancer is seen to be increased and Computed Tomography (CT) scan images were utilized in investigation to locate and classify lung cancer, also for determining the severity of those cancer. This study is aimed at employing pre-trained deep neural networks for classification of lung cancer i

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Noor Najah Ali, Aseel Hameed, Asanka G. Perera, and Ali Al Naji. "Custom YOLO Object Detection Model for COVID-19 Diagnosis." Journal of Techniques 5, no. 3 (2023): 92–100. http://dx.doi.org/10.51173/jt.v5i3.1174.

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The emergence and spread of the new coronavirus (COVID-19) poses a new public health threat to the entire world (SARS-CoV-2). This new virus is highly contagious and pathogenetically different from other mainstream respiratory viruses. Clinical staff can benefit from Computer Aided Diagnostics (CAD) systems that combine deep learning algorithms and image processing technologies as diagnostic tools for COVID-19. These tools also help to better understand the course of the disease. In most cases, medical staff and healthcare facilities would be more equipped to promptly diagnose COVID-19 for pat

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Fleury, Eduardo F. C., Ana Claudia Gianini, Karem Marcomini, and Vilmar Oliveira. "The Feasibility of Classifying Breast Masses Using a Computer-Assisted Diagnosis (CAD) System Based on Ultrasound Elastography and BI-RADS Lexicon." Technology in Cancer Research & Treatment 17 (January 1, 2018): 153303381876346. http://dx.doi.org/10.1177/1533033818763461.

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Objectives: To determine the applicability of a computer-aided diagnostic system strain elastography system for the classification of breast masses diagnosed by ultrasound and scored using the criteria proposed by the breast imaging and reporting data system ultrasound lexicon and to determine the diagnostic accuracy and interobserver variability. Methods: This prospective study was conducted between March 1, 2016, and May 30, 2016. A total of 83 breast masses subjected to percutaneous biopsy were included. Ultrasound elastography images before biopsy were interpreted by 3 radiologists with an

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Khan, Mudasir, Pir Masoom Shah, Izaz Ahmad Khan, et al. "IoMT-Enabled Computer-Aided Diagnosis of Pulmonary Embolism from Computed Tomography Scans Using Deep Learning." Sensors 23, no. 3 (2023): 1471. http://dx.doi.org/10.3390/s23031471.

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The Internet of Medical Things (IoMT) has revolutionized Ambient Assisted Living (AAL) by interconnecting smart medical devices. These devices generate a large amount of data without human intervention. Learning-based sophisticated models are required to extract meaningful information from this massive surge of data. In this context, Deep Neural Network (DNN) has been proven to be a powerful tool for disease detection. Pulmonary Embolism (PE) is considered the leading cause of death disease, with a death toll of 180,000 per year in the US alone. It appears due to a blood clot in pulmonary arte

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Nateghi, Ramin, Habibollah Danyali, Mohammad Sadegh Helfroush, and Ashkan Tashk. "Intelligent CAD System for Automatic Detection of Mitotic Cells from Breast Cancer Histology Slide Images Based on Teaching-Learning-Based Optimization." Computational Biology Journal 2014 (August 24, 2014): 1–9. http://dx.doi.org/10.1155/2014/970898.

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This paper introduces a computer-assisted diagnosis (CAD) system for automatic mitosis detection from breast cancer histopathology slide images. In this system, a new approach for reducing the number of false positives is proposed based on Teaching-Learning-Based optimization (TLBO). The proposed CAD system is implemented on the histopathology slide images acquired by Aperio XT scanner (scanner A). In TLBO algorithm, the number of false positives (falsely detected nonmitosis candidates as mitosis ones) is defined as a cost function and, by minimizing it, many of nonmitosis candidates will be r

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Yuan, R., J. Mayo, I. Streit, et al. "MA10.06 Randomized Clinical Trial with Computer Assisted Diagnosis (CAD) Versus Radiologist as First Reader of Lung Screening LDCT." Journal of Thoracic Oncology 14, no. 10 (2019): S287—S288. http://dx.doi.org/10.1016/j.jtho.2019.08.578.

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Alinsaif, Sadiq. "Unraveling Arrhythmias with Graph-Based Analysis: A Survey of the MIT-BIH Database." Computation 12, no. 2 (2024): 21. http://dx.doi.org/10.3390/computation12020021.

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Cardiac arrhythmias, characterized by deviations from the normal rhythmic contractions of the heart, pose a formidable diagnostic challenge. Early and accurate detection remains an integral component of effective diagnosis, informing critical decisions made by cardiologists. This review paper surveys diverse computational intelligence methodologies employed for arrhythmia analysis within the context of the widely utilized MIT-BIH dataset. The paucity of adequately annotated medical datasets significantly impedes advancements in various healthcare domains. Publicly accessible resources such as

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Tai, Hao-Chih, Kuen-Yuan Chen, Ming-Hsun Wu, King-Jen Chang, Chiung-Nien Chen, and Argon Chen. "Assessing Detection Accuracy of Computerized Sonographic Features and Computer-Assisted Reading Performance in Differentiating Thyroid Cancers." Biomedicines 10, no. 7 (2022): 1513. http://dx.doi.org/10.3390/biomedicines10071513.

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For ultrasound imaging of thyroid nodules, medical guidelines are all based on findings of sonographic features to provide clinicians management recommendations. Due to the recent development of artificial intelligence and machine learning (AI/ML) technologies, there have been computer-assisted detection (CAD) software devices available for clinical use to detect and quantify the sonographic features of thyroid nodules. This study is to validate the accuracy of the computerized sonographic features (CSF) by a CAD software device, namely, AmCAD-UT, and then to assess how the reading performance

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Eneogu, Rupert A., Ellen M. H. Mitchell, Chidubem Ogbudebe, et al. "Iterative evaluation of mobile computer-assisted digital chest x-ray screening for TB improves efficiency, yield, and outcomes in Nigeria." PLOS Global Public Health 4, no. 1 (2024): e0002018. http://dx.doi.org/10.1371/journal.pgph.0002018.

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Wellness on Wheels (WoW) is a model of mobile systematic tuberculosis (TB) screening of high-risk populations combining digital chest radiography with computer-aided automated detection (CAD) and chronic cough screening to identify presumptive TB clients in communities, health facilities, and prisons in Nigeria. The model evolves to address technical, political, and sustainability challenges. Screening methods were iteratively refined to balance TB yield and feasibility across heterogeneous populations. Performance metrics were compared over time. Screening volumes, risk mix, number needed to

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Bobokhojaev, OI, NN Abdulloev, ShD Khushvakhtov, and SG Shukurov. "Efficiency of Artificial Intelligence for Interpretation of Chest Radiograms in the Republic of Tajikistan." Journal of Pulmonology and Respiratory Research 8, no. 2 (2024): 069–73. http://dx.doi.org/10.29328/journal.jprr.1001064.

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The article presents data from recent publications and own data on screening studies with interpretation of chest radiographs using artificial intelligence CAD (Computer-Assisted Diagnosis), which, according to WHO recommendations, provides more accurate clinical thresholds for deciding who needs to take a sputum test. Another aspect of the WHO recommendations is the cost-effectiveness of CAD as a tool for triaging patients with tuberculosis symptoms in low-income countries with a high incidence of tuberculosis. Compared with smear microscopy and GeneXpert, without preliminary sorting, the use

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Okimoto, Eiko, Norihisa Ishimura, Kyoichi Adachi, Yoshikazu Kinoshita, Shunji Ishihara, and Tomohiro Tada. "Application of Convolutional Neural Networks for Diagnosis of Eosinophilic Esophagitis Based on Endoscopic Imaging." Journal of Clinical Medicine 11, no. 9 (2022): 2529. http://dx.doi.org/10.3390/jcm11092529.

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Subjective symptoms associated with eosinophilic esophagitis (EoE), such as dysphagia, are not specific, thus the endoscopic identification of suggestive EoE findings is quite important for facilitating endoscopic biopsy sampling. However, poor inter-observer agreement among endoscopists regarding diagnosis has become a complicated issue, especially with inexperienced practitioners. Therefore, we constructed a computer-assisted diagnosis (CAD) system using a convolutional neural network (CNN) and evaluated its performance as a diagnostic utility. A CNN-based CAD system was developed based on R

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