Table 2

Detailed training and test results based on fundus autofluorescence images for predicting the causative genes in inherited retinal disorder

Original classification with genetic diagnosis	Learning results			Predicted results
Original classification with genetic diagnosis	Training images (n)	Testing images (n)	Total	ABCA4 images (n)	EYS images (n)	RP1L1 images (n)	Normal images (n)	Sensitivity (%)	Specificity (%)	Accuracy (%)
Trial 1
ABCA4	27	10	37	10	–	–	–	100	100	–
EYS	25	10	35	–	7	–	3	70	100	–
RP1L1	33	10	43	–	–	7	3	70	97	–
Normal	31	12	43	–	–	1	11	92	80	–
Total	116	42	158	10	7	8	17	–	–	83
Trial 2
ABCA4	27	10	37	9	1	–	–	90	100	–
EYS	27	8	35	–	8	–	–	100	97	–
RP1L1	31	12	43	–	–	9	3	75	97	–
Normal	32	11	43	–	–	1	10	91	90	–
Total	117	41	158	9	9	10	13	–	–	88
Trial 3
ABCA4	28	9	37	9	–	–	–	100	91	–
EYS	25	10	35	3	7	–	–	70	100	–
RP1L1	32	11	43	–	–	6	5	55	100	–
Normal	32	11	43	–	–	–	11	100	83	–
Total	117	41	158	12	7	6	16	–	–	81
Trial 4
ABCA4	29	8	37	8	–	–	–	100	89	–
EYS	28	7	35	3	3	1	–	43	100	–
RP1L1	33	10	43	–	–	6	4	60	92	–
Normal	34	9	43	–	–	1	8	89	84	–
Total	124	34	158	11	3	8	12	–	–	74

In total, 82 subjects with molecularly confirmed inherited retinal disorders or no ocular diseases were ascertained: 19 with ABCA4 retinopathy, 18 with EYS retinopathy, 22 with RP1L1 retinopathy and 23 normal subjects. Subjects were randomly split following a 3: 1 ratio into training and test sets.
The commercially available deep learning tool, MedicMind, was applied to this four-class classification program.
The accuracy for each trial and the sensitivity and specificity for each category of each trial were calculated during the learning process, and this procedure was repeated four times with randomly assigned training/test sets to control for selection bias.