Table 1

Detailed training and test results based on fundus photography 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	30	11	41	8	–	3	–	73	100	–
EYS	70	24	94	1	22	1	–	92	98	–
RP1L1	49	16	65	–	–	16	–	100	88	–
Normal	43	16	59	1	1	2	12	75	100	–
Total	192	67	259	10	23	22	12	–	–	87
Trial 2
ABCA4	31	10	41	9	1	–	–	90	100	–
EYS	73	21	94	2	18	1	–	86	98	–
RP1L1	47	18	65	–	–	16	2	89	85	–
Normal	43	16	59	–	–	6	10	63	96	–
Total	194	65	259	11	19	23	12	–	–	82
Trial 3
ABCA4	31	10	41	10	–	–	–	100	100	–
EYS	66	28	94	1	24	–	3	86	100	–
RP1L1	47	18	65	–	–	16	2	89	98	–
Normal	42	17	59	–	–	1	16	94	91	–
Total	186	73	259	12	9	12	14	–	–	90
Trial 4
ABCA4	31	10	41	9	1	–	–	90	100	–
EYS	73	21	94	2	19	–	–	90	97	–
RP1L1	52	13	65	–	–	13	–	100	100	–
Normal	49	10	59	–	–	–	10	100	100	–
Total	205	54	259	11	20	13	10	–	–	94

In total, 132 subjects with molecularly confirmed inherited retinal disorders or no ocular diseases were ascertained: 21 with ABCA4 retinopathy, 48 with EYS retinopathy, 33 with RP1L1 retinopathy and 30 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.