Neuromyelitis spectrum disease (NMOSD) is an immune-mediated inflammatory demyelinating disease of the central nervous system. The breakdown of the blood-brain barrier (BBB), as an important link in the pathogenesis of NMOSD, has an important impact on the occurrence, development and prognosis of the disease. It is generally believed that the aquaporin 4 antibody produced in the peripheral circulation crosses the BBB cause damage to the central nervous system, and there are components involved in the destruction of BBB in the occurrence and development of NMOSD disease. At present, little is known about the molecular mechanism of BBB destruction in NMOSD lesions and there is still a lack of systematic theory. Further research and exploration of the regulatory mechanism of BBB permeability and the manifestation of barrier destruction in NMOSD diseases are of great significance for understanding the pathogenesis of NMOSD, so as to achieve early diagnosis and discover new therapeutic and preventive targets.
Objective To construct and evaluate a screening and diagnostic system based on color fundus images and artificial intelligence (AI)-assisted screening for optic neuritis (ON) and non-arteritic anterior ischemic optic neuropathy (NAION). MethodsA diagnostic test study. From 2016 to 2020, 178 cases 267 eyes of NAION patients (NAION group) and 204 cases 346 eyes of ON patients (ON group) were examined and diagnosed in Zhongshan Ophthalmic Center of Sun Yat-sen University; 513 healthy individuals of 1 160 eyes (the normal control group) with normal fundus by visual acuity, intraocular pressure and optical coherence tomography examination were collected from 2018 to 2020. All 2 909 color fundus images were as the data set of the screening and diagnosis system, including 730, 805, and 1 374 images for the NAION group, ON group, and normal control group, respectively. The correctly labeled color fundus images were used as input data, and the EfficientNet-B0 algorithm was selected for model training and validation. Finally, three systems for screening abnormal optic discs, ON, and NAION were constructed. The subject operating characteristic (ROC) curve, area under the ROC (AUC), accuracy, sensitivity, specificity, and heat map were used as indicators of diagnostic efficacy. ResultsIn the test data set, the AUC for diagnosing the presence of an abnormal optic disc, the presence of ON, and the presence of NAION were 0.967 [95% confidence interval (CI) 0.947-0.980], 0.964 (95%CI 0.938-0.979), and 0.979 (95%CI 0.958-0.989), respectively. The activation area of the systems were mainly located in the optic disc area in the decision-making process. ConclusionAbnormal optic disc, ON and NAION, and screening diagnostic systems based on color fundus images have shown accurate and efficient diagnostic performance.