Jianbo Mao1, Yuhao Luo2, Lei Liu3, Jimeng Lao1, Yirun Shao1, Min Zhang2, Caiyun Zhang1, Mingzhai Sun2,4, Lijun Shen1. 1. Eye Hospital of Wenzhou Medical University, Wenzhou Medical University, Wenzhou, China. 2. Department of Precision Machinery and Instrumentation, University of Science and Technology of China, Hefei, China. 3. Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China. 4. Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, China.
Abstract
BACKGROUND: The purpose of this study was to develop an automated diagnosis and quantitative analysis system for plus disease. The system provides a diagnostic decision but also performs quantitative analysis of the typical pathological features of the disease, which helps the physicians to make the best judgement and communicate the decisions. METHODS: The deep learning network provided segmentation of the retinal vessels and the optic disc (OD). Based on the vessel segmentation, plus disease was classified and tortuosity, width, fractal dimension and vessel density were evaluated automatically. RESULTS: The trained network achieved a sensitivity of 95.1% with 97.8% specificity for the diagnosis of plus disease. For detection of preplus or worse, the sensitivity and specificity were 92.4% and 97.4%. The quadratic weighted k was 0.9244. The tortuosities for the normal, preplus and plus groups were 3.61 ± 0.08, 5.95 ± 1.57 and 10.67 ± 0.50 (104 cm-3 ). The widths of the blood vessels were 63.46 ± 0.39, 67.21 ± 0.70 and 68.89 ± 0.75 μm. The fractal dimensions were 1.18 ± 0.01, 1.22 ± 0.01 and 1.26 ± 0.02. The vessel densities were 1.39 ± 0.03, 1.60 ± 0.01 and 1.64 ± 0.09 (%). All values were statistically different among the groups. After treatment for plus disease with ranibizumab injection, quantitative analysis showed significant changes in the pathological features. CONCLUSIONS: Our system achieved high accuracy of diagnosis of plus disease in retinopathy of prematurity. It provided a quantitative analysis of the dynamic features of the disease progression. This automated system can assist physicians by providing a classification decision with auxiliary quantitative evaluation of the typical pathological features of the disease.
BACKGROUND: The purpose of this study was to develop an automated diagnosis and quantitative analysis system for plus disease. The system provides a diagnostic decision but also performs quantitative analysis of the typical pathological features of the disease, which helps the physicians to make the best judgement and communicate the decisions. METHODS: The deep learning network provided segmentation of the retinal vessels and the optic disc (OD). Based on the vessel segmentation, plus disease was classified and tortuosity, width, fractal dimension and vessel density were evaluated automatically. RESULTS: The trained network achieved a sensitivity of 95.1% with 97.8% specificity for the diagnosis of plus disease. For detection of preplus or worse, the sensitivity and specificity were 92.4% and 97.4%. The quadratic weighted k was 0.9244. The tortuosities for the normal, preplus and plus groups were 3.61 ± 0.08, 5.95 ± 1.57 and 10.67 ± 0.50 (104 cm-3 ). The widths of the blood vessels were 63.46 ± 0.39, 67.21 ± 0.70 and 68.89 ± 0.75 μm. The fractal dimensions were 1.18 ± 0.01, 1.22 ± 0.01 and 1.26 ± 0.02. The vessel densities were 1.39 ± 0.03, 1.60 ± 0.01 and 1.64 ± 0.09 (%). All values were statistically different among the groups. After treatment for plus disease with ranibizumab injection, quantitative analysis showed significant changes in the pathological features. CONCLUSIONS: Our system achieved high accuracy of diagnosis of plus disease in retinopathy of prematurity. It provided a quantitative analysis of the dynamic features of the disease progression. This automated system can assist physicians by providing a classification decision with auxiliary quantitative evaluation of the typical pathological features of the disease.
Authors: Brittni A Scruggs; R V Paul Chan; Jayashree Kalpathy-Cramer; Michael F Chiang; J Peter Campbell Journal: Transl Vis Sci Technol Date: 2020-02-10 Impact factor: 3.283
Authors: Ali H Al-Timemy; Zahraa M Mosa; Zaid Alyasseri; Alexandru Lavric; Marcelo M Lui; Rossen M Hazarbassanov; Siamak Yousefi Journal: Transl Vis Sci Technol Date: 2021-12-01 Impact factor: 3.283