首页|New Artificial Intelligence Findings from Weill Cornell Medicine Reported (Early Detection of Optic Nerve Changes On Optical Coherence Tomography Using Deep Lea rning for Risk-stratification of Papilledema and Glaucoma)
New Artificial Intelligence Findings from Weill Cornell Medicine Reported (Early Detection of Optic Nerve Changes On Optical Coherence Tomography Using Deep Lea rning for Risk-stratification of Papilledema and Glaucoma)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on Artificial Intelligence is now available. According to news reporting from New York City, New York, by NewsRx journalists, research stated, "The use of artificial intelligence is beco ming more prevalence in medicine with numerous successful examples in ophthalmol ogy. However, much of the work has been focused on replicating the works of opht halmologists." The news correspondents obtained a quote from the research from Weill Cornell Me dicine, "Given the analytical potentials of artificial intelligence, it is plaus ible that artificial intelligence can detect microfeatures not readily distingui shed by humans. In this study, we tested the potential for artificial intelligen ce to detect early optic coherence tomography changes to predict progression tow ard papilledema or glaucoma when no significant changes are detected on optical coherence tomography by clinicians. Prediagnostic optical coherence tomography o f patients who developed papilledema (n = 93, eyes = 166) and glaucoma (n = 187, eyes = 327) were collected. Given discrepancy in average cup-to-disc ratios of the experimental groups, control groups for papilledema (n = 254, eyes = 379) an d glaucoma (n = 441, eyes = 739) are matched by cup-to-disc ratio. Publicly avai lable Visual Geometry Group-19 model is retrained using each experimental group and its respective control group to predict progression to papilledema or glauco ma. Images used for training include retinal nerve fiber layer thickness map, ex tracted vertical tomogram, ganglion cell thickness map, and ILM-RPE thickness ma p. Trained model was able to predict progression to papilledema with a precision of 0.714 and a recall of 0.769 when trained with retinal nerve fiber layer thic kness map, but not other image types. However, trained model was able to predict progression to glaucoma with a precision of 0.682 and recall of 0.857 when trai ned with extracted vertical tomogram, but not other image types. Area under prec ision-recall curve of 0.826 and 0.785 were achieved for papilledema and glaucoma models, respectively. Computational and analytical power of computers have beco me an invaluable part of our lives and research endeavors. Our proof-of-concept study showed that artificial intelligence (AI) algorithms have the potential to detect early changes on optical coherence tomography for prediction of progressi on that is not readily observed by clinicians."
New York CityNew YorkUnited StatesNorth and Cen-tral AmericaArtificial IntelligenceCranial Nerve Diseases and C onditionsEmerging TechnologiesEye Diseases and ConditionsGlaucomaHealth and MedicineImaging TechnologyMachine LearningOptic Nerve Diseases and Con ditionsOptical Coherence TomographyPapilledemaTechnologyWeill Cornell Me dicine
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