Zobrazeno 1 - 10 of 697 pro vyhledávání: '"M M, Hall"'
1
Akademický článek
Efficient automated error detection in medical data using deep-learning and label-clustering
Autor: T. V. Nguyen, S. M. Diakiw, M. D. VerMilyea, A. W. Dinsmore, M. Perugini, D. Perugini, J. M. M. Hall
Publikováno v: Scientific Reports, Vol 13, Iss 1, Pp 1-19 (2023)
Abstract Medical datasets inherently contain errors from subjective or inaccurate test results, or from confounding biological complexities. It is difficult for medical experts to detect these elusive errors manually, due to lack of contextual inform
Externí odkaz: https://doaj.org/article/2226d610176a46398c5a9f23564ec38c
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2
Akademický článek
A novel decentralized federated learning approach to train on globally distributed, poor quality, and protected private medical data
Autor: T. V. Nguyen, M. A. Dakka, S. M. Diakiw, M. D. VerMilyea, M. Perugini, J. M. M. Hall, D. Perugini
Publikováno v: Scientific Reports, Vol 12, Iss 1, Pp 1-12 (2022)
Abstract Training on multiple diverse data sources is critical to ensure unbiased and generalizable AI. In healthcare, data privacy laws prohibit data from being moved outside the country of origin, preventing global medical datasets being centralize
Externí odkaz: https://doaj.org/article/1199058a52154e45a2d3007a4508e14b
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4
Akademický článek
Automated detection of poor-quality data: case studies in healthcare
Autor: M. A. Dakka, T. V. Nguyen, J. M. M. Hall, S. M. Diakiw, M. VerMilyea, R. Linke, M. Perugini, D. Perugini
Publikováno v: Scientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
Abstract The detection and removal of poor-quality data in a training set is crucial to achieve high-performing AI models. In healthcare, data can be inherently poor-quality due to uncertainty or subjectivity, but as is often the case, the requiremen
Externí odkaz: https://doaj.org/article/92ef4fc958d544dcababaa3db903e24e
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5
A Novel Decentralized Federated Learning Approach to Train on Globally Distributed, Poor Quality, and Protected Private Medical Data
Autor: T V, Nguyen, M A, Dakka, S M, Diakiw, M D, VerMilyea, M, Perugini, J M M, Hall, D, Perugini
Training on multiple diverse data sources is critical to ensure unbiased and generalizable AI. In healthcare, data privacy laws prohibit data from being moved outside the country of origin, preventing global medical datasets being centralized for AI
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9dcebe052a83ceafdd0195e8789392b9
https://doi.org/10.21203/rs.3.rs-1371143/v1
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6
Development of an artificial intelligence model for predicting the likelihood of human embryo euploidy based on blastocyst images from multiple imaging systems during IVF
Autor: S M Diakiw, J M M Hall, M D VerMilyea, J Amin, J Aizpurua, L Giardini, Y G Briones, A Y X Lim, M A Dakka, T V Nguyen, D Perugini, M Perugini
Publikováno v: Human reproduction (Oxford, England). 37(8)
STUDY QUESTION Can an artificial intelligence (AI) model predict human embryo ploidy status using static images captured by optical light microscopy? SUMMARY ANSWER Results demonstrated predictive accuracy for embryo euploidy and showed a significant
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bbe6edd30ce9cccb69babf99c92427cb
https://pubmed.ncbi.nlm.nih.gov/35674312
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7
P–202 Past embryo viability is not always a good predictor of future pregnancy: dynamic viability suggests video has limited benefit over static images for AI assessment
Autor: D Perugini, Michelle Perugini, Jonathan M. M. Hall, S Diakiw, M A Dakka, Tan H. Nguyen
Publikováno v: Human Reproduction. 36
Study question Does embryo quality/viability change over time, suggesting the use of video for AI-based embryo quality assessment has limited benefit over single point-in-time images? Summary answer AI assessment of single static embryo images at mul
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::0e478e351724befe5721c96cd20820f9
https://doi.org/10.1093/humrep/deab130.201
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8
O-222 An artificial intelligence model that was trained on pregnancy outcomes for embryo viability assessment is highly correlated with Gardner Score
Autor: Tan H. Nguyen, M A Dakka, S Diakiw, D Perugini, M VerMilyea, Jonathan M. M. Hall, K Sorby, Michelle Perugini
Publikováno v: Human Reproduction. 36
Study question Do artificial intelligence (AI) models used to assess embryo viability (based on pregnancy outcomes) also correlate with known embryo quality measures such as Gardner score? Summary answer An AI for embryo viability assessment also cor
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::cf5974b8afb00272ff819bf6d2e8a82c
https://doi.org/10.1093/humrep/deab128.046
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9
P–228 AI-based assessment of embryo viability correlates with features of embryo ploidy
Autor: Jonathan M. M. Hall, Michelle Perugini, S Diakiw, Tan H. Nguyen, M A Dakka, Matthew David VerMilyea, D Perugini
Publikováno v: Human Reproduction. 36
Study question Do AI models used to assess embryo viability (based on pregnancy outcome) also correlate with known embryo quality measures such as ploidy status? Summary answer An AI for embryo viability assessment correlated with ploidy status, and
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::cd3d1282fd34173a519fd2e9f268fa46
https://doi.org/10.1093/humrep/deab130.227
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10
Development of an artificial intelligence-based assessment model for prediction of embryo viability using static images captured by optical light microscopy during IVF
Autor: Michelle Perugini, Jonathan M. M. Hall, A Picou, M VerMilyea, Tan H. Nguyen, S Diakiw, Annalee Murphy, Adrian Johnston, A Miller, D Perugini
Publikováno v: Human Reproduction (Oxford, England)
STUDY QUESTION Can an artificial intelligence (AI)-based model predict human embryo viability using images captured by optical light microscopy? SUMMARY ANSWER We have combined computer vision image processing methods and deep learning techniques to
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::77ab6354ce41f1dd8da74f451b4478c0
https://pubmed.ncbi.nlm.nih.gov/32240301
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