Multi-input Deep Learning Model for RP Diagnosis Using FVEP and Prior Knowledge

被引:0
|
作者
Chen, Yuguang [1 ,2 ,3 ,4 ,5 ,6 ]
Shen, Mei [2 ,3 ,4 ,5 ,6 ]
Lu, Dongmei [2 ,3 ,4 ,5 ,6 ]
Lin, Jun [9 ]
Hu, Jiaoyue [2 ,3 ,4 ,5 ,6 ,7 ]
Li, Shiying [2 ,3 ,4 ,5 ,6 ]
Liu, Zuguo [1 ,2 ,3 ,4 ,5 ,6 ,7 ,8 ]
机构
[1] Xiamen Univ, Inst Artificial Intelligence, Xiamen 361102, Fujian, Peoples R China
[2] Xiamen Univ, Dept Ophthalmol, Xiangan Hosp, Xiamen 361005, Fujian, Peoples R China
[3] Fujian Prov Key Lab Ophthalmol & Visual Sci, Xiamen 361005, Fujian, Peoples R China
[4] Fujian Engn & Res Ctr Eye Regenerat Med, Xiamen 361005, Fujian, Peoples R China
[5] Xiamen Univ, Eye Inst, Xiamen 361005, Fujian, Peoples R China
[6] Xiamen Univ, Sch Med, Xiamen 361005, Fujian, Peoples R China
[7] Xiamen Univ, Xiamen Eye Ctr, Xiamen 361005, Fujian, Peoples R China
[8] Univ South China, Dept Ophthalmol, Affiliated Hosp 1, Hengyang 421001, Hunan, Peoples R China
[9] Yongchuan Dist Peoples Hosp Chongqing, Dept Ophthalmol, Chongqing 402160, Peoples R China
来源
ADVANCED INTELLIGENT COMPUTING IN BIOINFORMATICS, PT I, ICIC 2024 | 2024年 / 14881卷
基金
中国国家自然科学基金;
关键词
FVEP; Retinitis Pigmentosa; Deep learning; Out-of-distribution detection;
D O I
10.1007/978-981-97-5689-6_25
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Retinitis Pigmentosa (RP) is a hereditary disease characterized by progressive damage to the visual pathway, ultimately leading to vision loss. Flash Visual Evoked Potential (FVEP) serves as an effective tool for diagnosing RP, and automatic classification of FVEP using deep learning can alleviate the workload of doctors and improve work efficiency. This study proposed a multi input neural network for RP and other anomaly recognition: MGPResNet. One branch of the model conducts full connection on manually crafted features to integrate them, while the other branch adopts a 1D ResNet as its basic architecture, it incorporates global convolutional blocks and pyramid pooling blocks to extract features from FVEP waveforms at deeper levels and different scales. Subsequently, the features extracted by the two branches are concatenated, followed by full connection and activation layers to output the classification probabilities. The model was validated on the FVEP datasets of two hospitals. The proposed method demonstrated excellent accuracy on clinical datasets, with an accuracy of 96.80%, average precision of 96.52%, average recall of 96.47%, and average F1_score of 96.49%. It validated the significant potential of deep learning in the analysis of visual electrophysiological signals, provided an important foundation and new insights for the future use of deep learning techniques in clinical diagnosis and treatment.
引用
收藏
页码:287 / 299
页数:13
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