Objective: To compare the diagnostic performance of an artificial intelligence deep learning system with that of expert neuro-ophthalmologists in classifying optic disc appearance. Methods: The deep learning system was previously trained and validated on 14,341 ocular fundus photographs from 19 international centers. The performance of the system was evaluated on 800 new fundus photographs (400 normal optic discs, 201 papilledema [disc edema from elevated intracranial pressure], 199 other optic disc abnormalities) and compared with that of 2 expert neuro-ophthalmologists who independently reviewed the same randomly presented images without clinical information. Area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity were calculated. Results: The system correctly classified 678 of 800 (84.7%) photographs, compared with 675 of 800 (84.4%) for Expert 1 and 641 of 800 (80.1%) for Expert 2. The system yielded areas under the receiver operating characteristic curve of 0.97 (95% confidence interval [CI] = 0.96–0.98), 0.96 (95% CI = 0.94–0.97), and 0.89 (95% CI = 0.87–0.92) for the detection of normal discs, papilledema, and other disc abnormalities, respectively. The accuracy, sensitivity, and specificity of the system's classification of optic discs were similar to or better than the 2 experts. Intergrader agreement at the eye level was 0.71 (95% CI = 0.67–0.76) between Expert 1 and Expert 2, 0.72 (95% CI = 0.68–0.76) between the system and Expert 1, and 0.65 (95% CI = 0.61–0.70) between the system and Expert 2. Interpretation: The performance of this deep learning system at classifying optic disc abnormalities was at least as good as 2 expert neuro-ophthalmologists. Future prospective studies are needed to validate this system as a diagnostic aid in relevant clinical settings. ANN NEUROL 2020;88:785–795.

Optic Disc Classification by Deep Learning versus Expert Neuro-Ophthalmologists

Amore G.;Carelli V.
Membro del Collaboration Group
;
La Morgia C.
Membro del Collaboration Group
;
2020

Abstract

Objective: To compare the diagnostic performance of an artificial intelligence deep learning system with that of expert neuro-ophthalmologists in classifying optic disc appearance. Methods: The deep learning system was previously trained and validated on 14,341 ocular fundus photographs from 19 international centers. The performance of the system was evaluated on 800 new fundus photographs (400 normal optic discs, 201 papilledema [disc edema from elevated intracranial pressure], 199 other optic disc abnormalities) and compared with that of 2 expert neuro-ophthalmologists who independently reviewed the same randomly presented images without clinical information. Area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity were calculated. Results: The system correctly classified 678 of 800 (84.7%) photographs, compared with 675 of 800 (84.4%) for Expert 1 and 641 of 800 (80.1%) for Expert 2. The system yielded areas under the receiver operating characteristic curve of 0.97 (95% confidence interval [CI] = 0.96–0.98), 0.96 (95% CI = 0.94–0.97), and 0.89 (95% CI = 0.87–0.92) for the detection of normal discs, papilledema, and other disc abnormalities, respectively. The accuracy, sensitivity, and specificity of the system's classification of optic discs were similar to or better than the 2 experts. Intergrader agreement at the eye level was 0.71 (95% CI = 0.67–0.76) between Expert 1 and Expert 2, 0.72 (95% CI = 0.68–0.76) between the system and Expert 1, and 0.65 (95% CI = 0.61–0.70) between the system and Expert 2. Interpretation: The performance of this deep learning system at classifying optic disc abnormalities was at least as good as 2 expert neuro-ophthalmologists. Future prospective studies are needed to validate this system as a diagnostic aid in relevant clinical settings. ANN NEUROL 2020;88:785–795.
Biousse V.; Newman N.J.; Najjar R.P.; Vasseneix C.; Xu X.; Ting D.S.; Milea L.B.; Hwang J.-M.; Kim D.H.; Yang H.K.; Hamann S.; Chen J.J.; Liu Y.; Wong T.Y.; Milea D.; Ronde-Courbis B.; Gohier P.; Miller N.; Padungkiatsagul T.; Poonyathalang A.; Suwan Y.; Vanikieti K.; Milea L.B.; Amore G.; Barboni P.; Carbonelli M.; Carelli V.; La Morgia C.; Romagnoli M.; Rougier M.-B.; Ambika S.; Komma S.; Fonseca P.; Raimundo M.; Karlesand I.; Alexander Lagreze W.; Sanda N.; Thumann G.; Aptel F.; Chiquet C.; Liu K.; Yang H.; Chan C.K.M.; Chan N.C.Y.; Cheung C.Y.; Chau Tran T.H.; Acheson J.; Habib M.S.; Jurkute N.; Yu-Wai-Man P.; Kho R.; Jonas J.B.; Sabbagh N.; Vignal-Clermont C.; Hage R.; Khanna R.K.; Aung T.; Cheng C.-Y.; Lamoureux E.; Loo J.L.; Najjar R.P.; Singhal S.; Ting D.; Tow S.; Jiang Z.; Fraser C.L.; Mejico L.J.; Fard M.A.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/794115
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