Development and validation of a deep-learning-based diagnostic model for drug-induced liver injury using computed tomography images

Table 1 Demographic characteristics of patients, n (%)/mean ± SD

Hospital group	Diagnostic group	Male	Age (years)
Zhongshan cohort	PA-HSOS (n = 39)	27 (69.23)	62.62 ± 12.33
	Budd-Chiari syndrome (n = 51)	29 (56.86)	44.98 ± 11.84
	Hepatitis B cirrhosis (n = 97)	73 (75.26)	53.29 ± 11.12
	Total (n = 187)	129 (68.98)	52.97 ± 13.02
Zhengzhou cohort	PA-HSOS (n = 28)	19 (67.86)	66.07 ± 9.25
	Budd-Chiari syndrome (n = 10)	6 (60.00)	55.9 ± 11.06
	Hepatitis B cirrhosis (n = 10)	8 (80)	49.60 ± 13.28
	Total (n = 48)	33 (68.75)	60.52 ± 12.43
Total	(n = 235)	162 (68.94)	54.37 ± 13.23

PA-HSOS: Pyrrolizidine-alkaloid-induced hepatic sinusoidal obstruction syndrome.

Table 2 Distribution of patient cohorts and disease types

	Zhongshan cohort		Zhengzhou cohort
	Training	Internal test	External test
PA-HSOS	31	8	28
Hepatitis B cirrhosis	78	19	10
Budd-Chiari syndrome	41	10	10
Total	150	37	48

PA-HSOS: Pyrrolizidine-alkaloid-induced hepatic sinusoidal obstruction syndrome.

Table 3 Patient-level performance of the deep learning-based diagnostic models on internal and external test cohort

Cohort	Model	AUC (95%CI)	ACC	SEN	SPE	PPV	NPV
Internal	Model 64	0.853 (0.678-1.000)	0.919	0.750	0.966	0.857	0.933
	Model 96	0.944 (0.829-1.000)	0.865	0.875	0.862	0.636	0.962
	Model 128	0.927 (0.797-1.000)	0.919	0.750	0.966	0.857	0.933
	Model 160	0.935 (0.813-1.000)	0.865	0.875	0.862	0.636	0.962
External	Model 64	0.871 (0.772-0.971)	0.854	0.821	0.900	0.920	0.783
	Model 96	0.873 (0.774-0.972)	0.854	0.821	0.900	0.920	0.783
	Model 128	0.893 (0.802-0.984)	0.854	0.821	0.900	0.920	0.783
	Model 160	0.891 (0.799-0.983)	0.875	0.857	0.900	0.923	0.818

AUC: Area under the curve; CI: Confidence interval; ACC: Accuracy; SEN: Sensitivity; SPE: Specificity; PPV: Positive predictive value; NPV: Negative predictive value.

Table 4 Patient-level performance of doctors and model 96 in the internal test cohort (n = 37)

Item	Accuracy (95%CI)	P value1	Sensitivity (95%CI)	P value	Specificity (95%CI)	P value	Youden index	Kappa value
Residents of internal medicine	0.541 (0.369-0.705)	0.002b	0.750 (0.349-0.968)	1.000	0.483 (0.294-0.675)	0.003b	0.233	0.147
Attending physicians	0.730 (0.559-0.862)	0.227	0.750 (0.349-0.968)	1.000	0.724 (0.528-0.873)	0.289	0.474	0.373
Residents of radiology	0.676 (0.502-0.820)	0.039a	0.875 (0.473-0.997)	1.000	0.621 (0.423-0.793)	0.039a	0.496	0.341
Attending radiologists	0.865 (0.712-0.955)	1.000	0.625 (0.245-0.915)	0.500	0.931 (0.772-0.992)	0.687	0.556	0.582
Model 96	0.865 (0.712-0.955)		0.875 (0.473-0.997)		0.862 (0.683-0.961)		0.737	0.649

¹The P values were compared with model 96 and presented without adjustment for multiple comparisons.

^aP < 0.05.

^bP < 0.01.

CI: Confidence interval.

Table 5 Comparison of doctors’ diagnostic results and time with and without model 96 assistance

Item	Professional category	Doctor only	Doctor + model 96	P value
Accuracy	RIM	0.541	0.757	0.021a
	AG	0.730	0.892	0.031a
	RR	0.676	0.811	0.063
	AR	0.865	0.973	0.125
Sensitivity	RIM	0.750	0.875	1.000
	AG	0.750	1.000
	RR	0.875	0.875	1.000
	AR	0.625	0.875	0.500
Specificity	RIM	0.483	0.759	0.008b
	AG	0.724	0.862	0.125
	RR	0.621	0.793	0.063
	AR	0.931	1.000
Time (seconds)	RIM	1807.00 ± 249.49	1561.33 ± 306.64	0.023a
	AG	1488.67 ± 462.48	924.67 ± 248.62	0.045a
	RR	916.33 ± 187.22	832.00 ± 179.10	0.004b
	AR	1071.00 ± 164.16	900.33+135.07	0.016a

^aP < 0.05 vs doctors only.

^bP < 0.01 vs doctors only.

RIM: Resident of internal medicine; AG: Attending gastroenterologist; RR: Resident of radiology; AR: Attending radiologist.