Predicting the generalization of computer aided detection (CADe) models for colonoscopy by Joel Shor, Carson McNeil, Yotam Intrator, Joseph R. Ledsam, Hiro-o Yamano, Daisuke Tsurumaru, Hiroki Kayama, Atsushi Hamabe, Koji Ando, Mitsuhiko Ota, Haruei Ogino, Hiroshi Nakase, Kaho Kobayashi, Masaaki Miyo, Eiji Oki, Ichiro Takemasa, Ehud Rivlin, Roman Goldenberg

“…We show that a "Masked Siamese Network" (MSN), trained to predict masked out regions of polyp images without labels, can predict the performance of Computer Aided Detection (CADe) of polyps on colonoscopies, without labels. …”
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Validation of Artificial Intelligence Computer-Aided Detection on Gastric Neoplasm in Upper Gastrointestinal Endoscopy by Hannah Lee, Jun-Won Chung, Sung-Cheol Yun, Sung Woo Jung, Yeong Jun Yoon, Ji Hee Kim, Boram Cha, Mohd Azzam Kayasseh, Kyoung Oh Kim

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Validation of Artificial Intelligence Computer-Aided Detection of Colonic Neoplasm in Colonoscopy by Hannah Lee, Jun-Won Chung, Kyoung Oh Kim, Kwang An Kwon, Jung Ho Kim, Sung-Cheol Yun, Sung Woo Jung, Ahmad Sheeraz, Yeong Jun Yoon, Ji Hee Kim, Mohd Azzam Kayasseh

“…In this study, we aimed to validate a previously developed artificial intelligence (AI) computer-aided detection (CADe) algorithm called ALPHAON^® and compare outcomes with previous studies that showed that AI outperformed and assisted endoscopists of diverse levels of expertise in detecting colon polyps. …”
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Leveraging paired mammogram views with deep learning for comprehensive breast cancer detection by Jae Won Seo, Young Jae Kim, Kwang Gi Kim

“…This study introduces paired mammogram view based-network(PMVnet), a novel algorithm designed to enhance breast lesion detection by integrating relational information from paired whole mammograms, addressing the limitations of current methods. …”
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Optimising computer aided detection to identify intra-thoracic tuberculosis on chest x-ray in South African children. by Megan Palmer, James A Seddon, Marieke M van der Zalm, Anneke C Hesseling, Pierre Goussard, H Simon Schaaf, Julie Morrison, Bram van Ginneken, Jaime Melendez, Elisabetta Walters, Keelin Murphy

“…Diagnostic tools for paediatric tuberculosis remain limited, with heavy reliance on clinical algorithms which include chest x-ray. Computer aided detection (CAD) for tuberculosis on chest x-ray has shown promise in adults. …”
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Effectiveness and cost-effectiveness of community-based TB screening algorithms using computer-aided detection (CAD) technology alone compared with CAD combined with point-of-care C reactive protein testing in Lesotho and South Africa: protocol for a paired screen-positive trial by Fabrizio Tediosi, Klaus Reither, Bram van Ginneken, Alastair van Heerden, Tinne Gils, Lutgarde Lynen, Josephine Muhairwe, Alfred Kipyegon Keter, Aita Signorell, Rediet Fikru Gebresenbet, Irene Ayakaka, Thomas Zoller, Fiona Vanobberghen, Mashaete Kamele, Thandanani Madonsela, Shannon Bosman, Anna Verjans, Bart Karl Jacobs, Curdin Brugger, Marina Antillon, Niklaus Daniel Labhardt, Harsh Vivek Harkare, Mamatlakeng Keitseng, Johanna Kurscheid, Keelin Murphy, Bulemba Katende, Rahel Milena Erhardt, Tracy R Glass

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Enhancing Cancer Diagnosis by Ismail Y. Maolood

“…With the aim of improving the effectiveness of cancer diagnosis, this paper introduces a new proposed method, computer-aided diagnosis, utilizing the level-set algorithm based on the edge detection approach for medical image segmentation. …”
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Revolutionizing Sperm Analysis with AI: A Review of Computer-Aided Sperm Analysis Systems by Francisco J. Baldán, Diego García-Gil, Carlos Fernandez-Basso

“…This review focuses on integrating AI with Computer-Aided Sperm Analysis (CASA) systems to improve assessments of sperm motility, morphology, and DNA integrity. …”
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Segmentation versus detection: Development and evaluation of deep learning models for prostate imaging reporting and data system lesions localisation on Bi‐parametric prostate magn... by Zhe Min, Fernando J. Bianco, Qianye Yang, Wen Yan, Ziyi Shen, David Cohen, Rachael Rodell, Dean C. Barratt, Yipeng Hu

“…Most existing computer‐aided diagnosis systems adopt segmentation methods while object detection approaches recently show promising results. …”
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An explainable analytical approach to heart attack detection using biomarkers and nature-inspired algorithms by Maithri Bairy, Krishnaraj Chadaga, Niranjana Sampathila, R. Vijaya Arjunan, G. Muralidhar Bairy

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Isfahan Artificial Intelligence Event 2023: Macular Pathology Detection Competition by Farnaz Sedighin, Maryam Monemian, Zahra Zojaji, Ahmadreza Montazerolghaem, Mohammad Amin Asadinia, Seyed Mojtaba Mirghaderi, Seyed Amin Naji Esfahani, Mohammad Kazemi, Reza Mokhtari, Maryam Mohammadi, Mohadese Ramezani, Mahnoosh Tajmirriahi, Hossein Rabbani

“…Background: Computer-aided diagnosis (CAD) methods have become of great interest for diagnosing macular diseases over the past few decades. …”
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Hybrid convolutional neural network optimized with an artificial algae algorithm for glaucoma screening using fundus images by M. Shanmuga Eswari, S. Balamurali, Lakshmana Kumar Ramasamy

“…Segmented and classified images can be directed to the health care system to assess patients’ progress. This computer-aided decision support system will be useful for optometrists.…”
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Artificial Intelligence in Gastroenterology: A Comprehensive Review by Raju Vaishya, Mahesh Kumar Goenka, Anupam Sibal, Sujoy Kar, Sangita Reddy

“…Through advanced technologies like Computer-Aided detection (CADe) and Computer-Aided diagnosis (CADx) using deep learning models, AI significantly improves polyp detection rates and reduces miss rates, with reported performances of 95% detection and 50% miss rate reduction. …”
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A Novel Machine Learning Model for the Automated Diagnosis of Nasal Pathology in Canine Patients by Andreea Istrate, Radu Constantinescu, Lithicka Anandavel, Shraddha Rajeshkumar Tandel, Simon Dye, Charlotte Dye

“…Recent years have seen remarkable advancement in computer-aided detection systems in human medicine, with machine and deep learning techniques being successfully applied for the identification and accurate classification of intranasal pathology. …”
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