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Artificial Intelligence–Assisted Early Detection of Retinitis Pigmentosa — the Most Common Inherited Retinal Degeneration

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Abstract

The purpose of this study was to detect the presence of retinitis pigmentosa (RP) based on color fundus photographs using a deep learning model. A total of 1670 color fundus photographs from the Taiwan inherited retinal degeneration project and National Taiwan University Hospital were acquired and preprocessed. The fundus photographs were labeled RP or normal and divided into training and validation datasets (n = 1284) and a test dataset (n = 386). Three transfer learning models based on pre-trained Inception V3, Inception Resnet V2, and Xception deep learning architectures, respectively, were developed to classify the presence of RP on fundus images. The model sensitivity, specificity, and area under the receiver operating characteristic (AUROC) curve were compared. The results from the best transfer learning model were compared with the reading results of two general ophthalmologists, one retinal specialist, and one specialist in retina and inherited retinal degenerations. A total of 935 RP and 324 normal images were used to train the models. The test dataset consisted of 193 RP and 193 normal images. Among the three transfer learning models evaluated, the Xception model had the best performance, achieving an AUROC of 96.74%. Gradient-weighted class activation mapping indicated that the contrast between the periphery and the macula on fundus photographs was an important feature in detecting RP. False-positive results were mostly obtained in cases of high myopia with highly tessellated retina, and false-negative results were mostly obtained in cases of unclear media, such as cataract, that led to a decrease in the contrast between the peripheral retina and the macula. Our model demonstrated the highest accuracy of 96.00%, which was comparable with the average results of 81.50%, of the other four ophthalmologists. Moreover, the accuracy was obtained at the same level of sensitivity (95.71%), as compared to an inherited retinal disease specialist. RP is an important disease, but its early and precise diagnosis is challenging. We developed and evaluated a transfer-learning-based model to detect RP from color fundus photographs. The results of this study validate the utility of deep learning in automating the identification of RP from fundus photographs.

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Abbreviations

AI:

Artificial intelligence

AMD:

Age-related macular degeneration

AUROC:

Area under the receiver operating characteristic

CNN:

Convolutional neural network

DR:

Diabetic retinopathy

grad-CAM:

Gradient class activation map

IRD:

Inherited retinal disease

OCT:

Optical coherence tomography

ROC:

Receiver operating characteristic

RP:

Retinitis pigmentosa

TIP:

Taiwan inherited retinal degeneration project

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Acknowledgements

We thank Dr. Chia-Yi Cheng, Dr. Mei-Chi Tsui, and Dr. Hsuan-Chieh Lin for the help with collecting data in this study.

Funding

This study is supported by the research grants: NTU Medical Genie — AI Decision Support System for Precision Medicine (Subproject 4: AI Technologies for Precision Medicine) from National Taiwan University Hospital, Taipei, Taiwan.

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Authors and Affiliations

  1. Department of Ophthalmology, College of Medicine, National Taiwan University, Taipei, Taiwan

    Ta-Ching Chen, Yu-Shu Huang, Chung-May Yang, Fung-Rong Hu & Chang-Hao Yang

  2. Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan

    Ta-Ching Chen

  3. Department of Computer Science and Information Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, Taiwan

    Wee Shin Lim, Shu-I Chiu & Jyh-Shing Roger Jang

  4. Case Western Reserve University School of Medicine, Cleveland, OH, USA

    Victoria Y. Wang

  5. Department of Ophthalmology, National Taiwan University Hospital Hsin-Chu Branch, Hsinchu, Taiwan

    Mei-Lan Ko

  6. Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan

    Feipei Lai

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  1. Ta-Ching Chen
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  2. Wee Shin Lim
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  10. Jyh-Shing Roger Jang
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  11. Chang-Hao Yang
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Correspondence to Jyh-Shing Roger Jang or Chang-Hao Yang.

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Chen, TC., Lim, W.S., Wang, V.Y. et al. Artificial Intelligence–Assisted Early Detection of Retinitis Pigmentosa — the Most Common Inherited Retinal Degeneration. J Digit Imaging 34, 948–958 (2021). https://doi.org/10.1007/s10278-021-00479-6

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  • DOI: https://doi.org/10.1007/s10278-021-00479-6

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