Utility of a deep learning model and a clinical model for predicting bleeding after endoscopic submucosal dissection in patients with early gastric cancer

Table 4 Decile of risk probability based on deep learning model and clinical model

Decile	Risk categories	Deep learning				Clinical model
		Score1	Patients	Bleeding	Rate (%)	Score1	Patients	Bleeding	Rate (%)
Development set
1	Low	25.7	451	11	2.4	8.4	451	12	2.6
2		29.1	451	15	3.3	12.2	451	15	3.3
3		32.5	451	6	1.3	12.4	451	12	2.6
4		35.9	450	17	3.8	12.7	450	20	4.4
5	Intermediate	40.2	450	27	6.0	14.8	450	34	7.6
6		45.3	450	29	6.4	16.6	450	15	3.3
7		50.8	450	36	8.0	23.3	450	24	5.3
8		57.5	450	24	5.3	24.6	450	32	7.1
9	High	67.2	450	41	9.1	31.0	450	54	12.0
10		197.0	450	63	14.0	122.0	450	51	11.3
Validation set
	Low	35.9	411	9	2.2	12.7	956	38	4.0
	Intermediate	57.5	466	18	3.9	24.5	137	12	8.8
	High	147.0	249	29	11.6	155.0	33	6	18.2

¹The score is calculated with probability multiplied by 1000 and presented as maximum cutoff in each decile.

Decile 1^st to 4^th: Low-risk category. Decile 5^th to 8^th: Intermediate-risk category. Decile 9^th to 10^th: High-risk category. The Cochran–Armitage test for trend was performed.