Campbell, J Peter Chiang, Michael F Chen, Jimmy S Moshfeghi, Darius M Nudleman, Eric Ruambivoonsuk, Paisan Cherwek, Hunter Cheung, Carol Y Singh, Praveer Kalpathy-Cramer, Jayashree
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Published in
Ophthalmology
To validate a vascular severity score as an appropriate output for artificial intelligence (AI) Software as a Medical Device (SaMD) for retinopathy of prematurity (ROP) through comparison with ordinal disease severity labels for stage and plus disease assigned by the International Classification of Retinopathy of Prematurity, Third Edition (ICROP3)...
Passamonti, Francesco Corrao, Giovanni Castellani, Gastone Mora, Barbara Maggioni, Giulia Gale, Robert Peter Della Porta, Matteo Giovanni
Published in
Blood reviews
Most national health-care systems approve new drugs based on data of safety and efficacy from large randomized clinical trials (RCTs). Strict selection biases and study-entry criteria of subjects included in RCTs often do not reflect those of the population where a therapy is intended to be used. Compliance to treatment in RCTs also differs conside...
Bilgic, Elif Gorgy, Andrew Yang, Alison Cwintal, Michelle Ranjbar, Hamed Kahla, Kalin Reddy, Dheeksha Li, Kexin Ozturk, Helin Zimmermann, Eric
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Published in
American journal of surgery
Technology-enhanced teaching and learning, including Artificial Intelligence (AI) applications, has started to evolve in surgical education. Hence, the purpose of this scoping review is to explore the current and future roles of AI in surgical education. Nine bibliographic databases were searched from January 2010 to January 2021. Full-text article...
Sakamoto, Takashi Goto, Tadahiro Fujiogi, Michimasa Kawarai Lefor, Alan
Published in
Surgery today
Machine learning (ML) is a collection of algorithms allowing computers to learn directly from data without predetermined equations. It is used widely to analyze "big data". In gastrointestinal surgery, surgeons deal with various data such as clinical parameters, surgical videos, and pathological images, to stratify surgical risk, perform safe surge...
Chen, Simon B Novoa, Roberto A
Published in
Seminars in diagnostic pathology
Artificial intelligence (AI), including deep learning methods that leverage neural network-based algorithms, hold significant promise for dermatopathology and other areas of diagnostic pathology in research and clinical practice. There has been significant progress over past several years in applying AI to analyzing digital histopathology images fo...
Kaushal, Karanvir Sarma, Phulan Rana, S V Medhi, Bikash Naithani, Manisha
Published in
Journal of biomolecular structure & dynamics
To elucidate the role of artificial intelligence (AI) in therapeutics for coronavirus disease 2019 (COVID-19). Five databases were searched (December 2019-May 2020). We included both published and pre-print original articles in English that applied AI, machine learning or deep learning in drug repurposing, novel drug discovery, vaccine and antibody...
Cortes-Briones, Jose A Tapia-Rivas, Nicolas I D'Souza, Deepak Cyril Estevez, Pablo A
Published in
Schizophrenia research
Despite years of research, the mechanisms governing the onset, relapse, symptomatology, and treatment of schizophrenia (SZ) remain elusive. The lack of appropriate analytic tools to deal with the heterogeneity and complexity of SZ may be one of the reasons behind this situation. Deep learning, a subfield of artificial intelligence (AI) inspired by ...
Başaran, Melike Çelik, Özer Bayrakdar, Ibrahim Sevki Bilgir, Elif Orhan, Kaan Odabaş, Alper Aslan, Ahmet Faruk Jagtap, Rohan
Published in
Oral radiology
The goal of this study was to develop and evaluate the performance of a new deep-learning (DL) artificial intelligence (AI) model for diagnostic charting in panoramic radiography. One thousand eighty-four anonymous dental panoramic radiographs were labeled by two dento-maxillofacial radiologists for ten different dental situations: crown, pontic, r...
Cai, Shengping Chen, Yang Zhao, Shixuan He, Dehuai Li, Yongjie Xiong, Nian Li, Zhidan Hu, Shaoping
Published in
European radiology
To develop a dynamic 3D radiomics analysis method using artificial intelligence technique for automatically assessing four disease stages (i.e., early, progressive, peak, and absorption stages) of COVID-19 patients on CT images. The dynamic 3D radiomics analysis method was composed of three AI algorithms (the lung segmentation, lesion segmentation,...
Kihara, Yuka Montesano, Giovanni Chen, Andrew Amerasinghe, Nishani Dimitriou, Chrysostomos Jacob, Aby Chabi, Almira Crabb, David P Lee, Aaron Y
Published in
Ophthalmology
To develop and validate a deep learning (DL) system for predicting each point on visual fields (VFs) from disc and OCT imaging and derive a structure-function mapping. Retrospective, cross-sectional database study. A total of 6437 patients undergoing routine care for glaucoma in 3 clinical sites in the United Kingdom. OCT and infrared reflectance (...
