Artificial Intelligence–Assisted Early Detection of Retinitis Pigmentosa — the Most Common Inherited Retinal Degeneration.
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 p...
| Publicado en: | Journal of Digital Imaging Vol. 34; no. 4; pp. 948 - 959 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | algorithm equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=152559507&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 152559507 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2021 vid: 34 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 152559507 151414457 152559507 152559507 10.1007/s10278-021-00479-6 152559507 ppf: 948 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Artificial Intelligence–Assisted Early Detection of Retinitis Pigmentosa — the Most Common Inherited Retinal Degeneration. aug: au: Chen, Ta-Ching Lim, Wee Shin Wang, Victoria Y. Ko, Mei-Lan Chiu, Shu-I Huang, Yu-Shu Lai, Feipei Yang, Chung-May Hu, Fung-Rong Jang, Jyh-Shing Roger Yang, Chang-Hao affil: Department of Ophthalmology, College of Medicine, National Taiwan University, Taipei, Taiwan sug: subj: Retinitis Pigmentosa Diagnosis Early Diagnosis Retina Pathology Photography Color Ophthalmoscopy Methods Deep Learning Methods Human Retinal Diseases Classification Registries, Disease Taiwan Academic Medical Centers Sensitivity and Specificity ROC Curve Ophthalmologists Models, Statistical Myopia False Negative Results Descriptive Statistics Ophthalmoscopy Equipment and Supplies Automation Image Processing, Computer Assisted ab: 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. pubtype: Academic Journal doctype: algorithm equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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