Deep learning algorithms for automatic detection of pterygium using anterior segment photographs from slit-lamp and hand-held cameras

Further Publications

Detection of shallow anterior chamber depth from two-dimensional anterior segment photographs using deep learning

BMC Ophthalmol

Qian Z, Xie X, Yang J, Ye H, Wang Z, Chen J, Liu H, Liang J, Jiang L, Zheng C, Chen X

doi: 10.1186/s12886-021-02104-0

Macular DiseasesRetinal Diseases

Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.

JAMA.

Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, Kim R, Raman R, Nelson PC, Mega JL, Webster DR

doi: 10.1001/jama.2016.17216.

Glaucoma and Ocular PressureFurther Publications

Anterior segment biometric measurements explain misclassifications by a deep learning classifier for detecting gonioscopic angle closure

Br J Ophthalmol

Shen A, Chiang M, Pardeshi AA, McKean-Cowdin R, Varma R, Xu BY

doi: 10.1136/bjophthalmol-2021-319058

COMMENTED

Glaucoma and Ocular Pressure

Assessment of a Segmentation-Free Deep Learning Algorithm for Diagnosing Glaucoma From Optical Coherence Tomography Scans

★ 1 Recommended by Prof. Dr emer. Josef Flammer, Faculty Member

★ 3 Recommended by Prof. Dr Ke Yao, Faculty Member

★ 3 Recommended and commented by Dr habil. Gábor Márk Somfai, PhD, Faculty Member

JAMA Ophthalmol.

Thompson AC, Jammal AA, Berchuck SI, Mariottoni EB, Medeiros FA

doi: 10.1001/jamaophthalmol.2019.5983.

COMMENTED

Retinal Diseases

Using a deep learning algorithm and integrated gradients explanation to assist grading for diabetic retinopathy

Ophthalmology.

Sayres R, Taly A, Rahimy E, Blumer K, Coz D, Hammel N, Krause J, Narayanaswamy A, Rastegar Z, Wu D, Xu S, Barb S, Joseph A, Shumski M, Smith J, Sood AB, Corrado GS, Peng L, Webster DR

doi: 10.1016/j.ophtha.

COMMENTED

Glaucoma and Ocular Pressure

Predicting Glaucoma before Onset Using Deep Learning

★ 1 Recommended and commented by Prof. Dr emer. Josef Flammer, Faculty Member

Ophthalmol Glaucoma

Thakur A, Goldbaum M, Yousefi S

doi: 10.1016/j.ogla.2020.04.012

Retinal Diseases

Validation and Clinical Applicability of Whole-Volume Automated Segmentation of Optical Coherence Tomography in Retinal Disease Using Deep Learning

JAMA Ophthalmol

Wilson M, Chopra R, Wilson MZ, Cooper C, MacWilliams P, Liu Y, Wulczyn E, Florea D, Hughes CO, Karthikesalingam A, Khalid H, Vermeirsch S, Nicholson L, Keane PA, Balaskas K, Kelly CJ

doi: 10.1001/jamaophthalmol.2021.2273

Macular Diseases

Detection of oedema on optical coherence tomography images using deep learning model trained on noisy clinical data

Acta Ophthalmol

Potapenko I, Kristensen M, Thiesson B, Ilginis T, Lykke Sørensen T, Nouri Hajari J, Fuchs J, Hamann S, la Cour M

doi: 10.1111/aos.14895

COMMENTED

Macular Diseases

Use of Deep Learning for Detailed Severity Characterization and Estimation of 5-Year Risk Among Patients With Age-Related Macular Degeneration

JAMA Ophthalmol.

Burlina PM, Joshi N, Pacheco KD, Freund DE, Kong J, Bressler NM

doi: 10.1001/jamaophthalmol.

Related content

Detection of shallow anterior chamber depth from two-dimensional anterior segment photographs using deep learning

Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.

Anterior segment biometric measurements explain misclassifications by a deep learning classifier for detecting gonioscopic angle closure

Assessment of a Segmentation-Free Deep Learning Algorithm for Diagnosing Glaucoma From Optical Coherence Tomography Scans

Using a deep learning algorithm and integrated gradients explanation to assist grading for diabetic retinopathy

Predicting Glaucoma before Onset Using Deep Learning

Validation and Clinical Applicability of Whole-Volume Automated Segmentation of Optical Coherence Tomography in Retinal Disease Using Deep Learning

Detection of oedema on optical coherence tomography images using deep learning model trained on noisy clinical data

Use of Deep Learning for Detailed Severity Characterization and Estimation of 5-Year Risk Among Patients With Age-Related Macular Degeneration