Diagnostic value of artificial intelligence automatic detection systems for breast BI-RADS 4 nodules

Table 1 results of 107 breast cases

Pathological results	Number of nodules
Benign
Fibroadenoma	32
Adenosis	18
Granulomatous mastitis	3
Intraductal papilloma	2
Galactocele	2
Plasma cell mastitis	1
Phyllodes tumor	1
Sclerosing adenosis	1
Nodular fasciitis	1
Malignant
Invasive ductal carcinoma	31
Intraductal papillary carcinoma	5
Invasive lobular carcinoma	4
Encapsulated papillary carcinoma	2
Mucinous carcinoma	1
Undifferentiated carcinoma	1
Malignant phyllodes tumor	1
Solid papillary carcinoma	1

Table 2 Diagnostic efficiency of four diagnostic models

Inspection method	Pathology		Susceptibility (%)	Specificity (%)	Accuracy (%)	Jordan index	Positive predictive value (%)	Negative predictive value (%)
	Benign (n = 61)	Malignant (n = 46)
Conventional ultrasound BI-RADS classification			84.78	57.21	74.44	0.5199	61.11	85.42
Benign (n = 48)	41	7
Malignant (n = 59)	20	39
AI-SONIC Breast system			86.96	75.41	80.37	0.6237	72.73	88.46
Benign (n = 52)	46	6
Malignant (n = 53)	15	40
AI-SONIC Breast system combined BI-RADS classification of conventional ultrasound			80.43	90.16	85.98	0.7059	86.05	85.94
Benign (n = 64)	55	9
Malignant (n = 43)	6	37
Adjusted BI-RADS classification			93.48	67.21	78.50	0.6069	68.25	93.18
Benign (n = 44)	41	3
Malignant (n = 63)	20	43

Table 3 BI-RADS classification distribution and risk prediction before and after adjustment

Inspection method	Biopsy rate (%)	Malignancy risk (%)	Cancer detection rate (%)
BI-RADS classification before adjustment	100	0	42.99
4A (n = 48)
4B (n = 20)
4C (n = 39)
Adjusted BI-RADS classification	67.29	1.87	61.11
3 (n = 35)
4A (n = 9)
4B (n = 20)
4C (n = 11)
5 (n = 32)