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3 results on '"Courbis, B."'

2. Optic Disc Classification by Deep Learning versus Expert Neuro-Ophthalmologists

Author

Hui Yang, Piero Barboni, Carol Y. Cheung, Rabih Hage, Catherine Vignal-Clermont, Isabelle Karlesand, Kaiqun Liu, Raoul K. Khanna, Florent Aptel, Luis J. Mejico, Donghyun Kim, Pedro Fonseca, Giulia Amore, Marie Bénédicte Rougier, Nancy J. Newman, Christophe Chiquet, Maged S. Habib, Tin Aung, Gabriele Thumann, Daniel S. Ting, Carmen K.M. Chan, Dan Milea, Léonard B. Milea, Jost B. Jonas, Ching-Yu Cheng, Selvakumar Ambika, Miguel Raimundo, Raymond P. Najjar, Yong Liu, Xinxing Xu, Caroline Vasseneix, Tanyatuth Padungkiatsagul, Sharon Tow, Nouran Sabbagh, Yanin Suwan, John J. Chen, Patrick Yu-Wai-Man, Ecosse L. Lamoureux, Shweta Singhal, Anuchit Poonyathalang, James Acheson, Philippe Gohier, Jing Liang Loo, Masoud Aghsaei Fard, Barnabé Rondé-Courbis, Steffen Hamann, Daniel S W Ting, Nicolae Sanda, Michele Carbonelli, Valerio Carelli, Hee Kyung Yang, Valérie Biousse, Clare L. Fraser, Chiara La Morgia, Swetha Komma, Tien Yin Wong, Jeong Min Hwang, Neringa Jurkute, Richard Kho, Neil R. Miller, Thi Ha Chau Tran, Zhubo Jiang, Kavin Vanikieti, Noel C.Y. Chan, Wolf A. Lagrèze, Martina Romagnoli, 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., 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., Singhal S., Ting D., Tow S., Jiang Z., Fraser C.L., Mejico L.J., Fard M.A., Sanda, Nicolae, and Thumann, Gabriele

Subjects
0301 basic medicine, Adult, Male, medicine.medical_specialty, genetic structures, Ophthalmological, Optic Disk, Optic disk, Fundus (eye), Diagnostic Techniques, Ophthalmological, Sensitivity and Specificity, 03 medical and health sciences, 0302 clinical medicine, Deep Learning, Ophthalmology, Image Interpretation, Computer-Assisted, Computer-Assisted/methods, medicine, Humans, Papilledema, Image Interpretation, Aged, Receiver operating characteristic, Ophthalmologists, business.industry, Deep learning, Ophthalmologist, Middle Aged, eye diseases, Confidence interval, ddc:616.8, Diagnostic Techniques, 030104 developmental biology, medicine.anatomical_structure, Neurology, Female, Neurology (clinical), Artificial intelligence, medicine.symptom, business, 030217 neurology & neurosurgery, Human, Optic disc abnormalities, Optic disc
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.

Published
2020

3. A Plasma Metabolomic Profiling of Exudative Age-Related Macular Degeneration Showing Carnosine and Mitochondrial Deficiencies.

Author

Chao de la Barca JM, Rondet-Courbis B, Ferré M, Muller J, Buisset A, Leruez S, Plubeau G, Macé T, Moureauzeau L, Chupin S, Tessier L, Blanchet O, Lenaers G, Procaccio V, Mirebeau-Prunier D, Simard G, Gohier P, Miléa D, and Reynier P

Abstract

To determine the plasma metabolomic profile of exudative age-related macular degeneration (AMD), we performed a targeted metabolomics study on the plasma from patients ( n = 40, mean age = 81.1) compared to an age- and sex-matched control group ( n = 40, mean age = 81.8). All included patients had documented exudative AMD, causing significant visual loss (mean logMAR visual acuity = 0.63), compared to the control group. Patients and controls did not differ in terms of body mass index and co-morbidities. Among the 188 metabolites analyzed, 150 (79.8%) were accurately measured. The concentrations of 18 metabolites were significantly modified in the AMD group, but only six of them remained significantly different after Benjamini-Hochberg correction. Valine, lysine, carnitine, valerylcarnitine and proline were increased, while carnosine, a dipeptide disclosing anti-oxidant and anti-glycating properties, was, on average, reduced by 50% in AMD compared to controls. Moreover, carnosine was undetectable for 49% of AMD patients compared to 18% in the control group ( p -value = 0.0035). Carnitine is involved in the transfer of fatty acids within the mitochondria; proline, lysine and valerylcarnitine are substrates for mitochondrial electrons transferring flavoproteins, and proline is one of the main metabolites supplying energy to the retina. Overall, our results reveal six new metabolites involved in the plasma metabolomic profile of exudative AMD, suggesting mitochondrial energetic impairments and carnosine deficiency.

Published
2020
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