Application of artificial intelligence to CT diagnosis of thoracic traumatic rib sites: a preliminary study

doi:10.3969/j.issn.1674-8115.2021.07.012

Abstract

Abstract: Objective

·To assess the detection rate of an artificial intelligence (AI) software for acute traumatic rib fractures on chest computed tomograph (CT) images.

Methods

·A consecutive cohort of CT images of the patients with acute chest trauma were collected from August 2019 to September 2019 (n=393). The reference standard was defined as the consensus reading results of three radiologist experts. At the lesion level, the sensitivity was studied, including all lesions and different types of rib fractures (i.e., displaced rib fractures, mild fractures, and cortical distortion). The sensitivity was also studied at both patient and rib levels.

Results

·At the lesion level, the total rib fracture detection sensitivity of AI was 81.75%. For displaced rib fractures, the sensitivity was 94.85%, which was the highest among the three types of fractures (P=0.000). When all types of rib fractures were taken as the object of study, the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of AI were 82.45%, 98.33%, 75.30% and 98.91% respectively at the rib level, and 90.91%, 76.21%, 77.63% and 90.23% respectively at the patient level. When displaced rib fracture were taken as the object, the sensitivity, specificity, PPV and NPV of AI were 94.57%, 98.26%, 51.94% and 99.89% respectively at the rib level, and 95.56%, 74.59%, 52.76% and 98.26% at the patient level.

Conclusion

·AI software has high sensitivity in detecting the fracture sites, and it could be potentially used for screening the thorax CT images in patients with acute chest trauma.

Key words: rib fracture, computed tomography, artificial intelligence, emergency radiology

CLC Number:

R445.3

Xiang LIU, Hui-hui XIE, Yu-feng XU, Xiao-feng TAO, Lin LIU, Di-jia WU, Xiao-ying WANG. Application of artificial intelligence to CT diagnosis of thoracic traumatic rib sites: a preliminary study[J]. JOURNAL OF SHANGHAI JIAOTONG UNIVERSITY (MEDICAL SCIENCE), 2021, 41(7): 920-925.

Figures/Tables 9

TrendMD

References 23

1	Liman ST, Kuzucu A, Tastepe AI, et al. Chest injury due to blunt trauma[J]. Eur J Cardiothorac Surg, 2003, 23(3): 374-378.
2	Ivey KM, White CE, Wallum TE, et al. Thoracic injuries in US combat casualties: a 10-year review of Operation Enduring Freedom and Iraqi Freedom[J]. J Trauma Acute Care Surg, 2012, 73(6 ): S514-S519.
3	Bemelman M, de Kruijf MW, van Baal M, et al. Rib fractures: to fix or not to fix? An evidence-based algorithm[J]. Korean J Thorac Cardiovasc Surg, 2017, 50(4): 229-234.
4	de Jong MB, Kokke MC, Hietbrink F, et al. Surgical management of rib fractures: strategies and literature review[J]. Scand J Surg, 2014, 103(2): 120-125.
5	Chung JH, Cox CW, Mohammed TL, et al. ACR appropriateness criteria blunt chest trauma[J]. J Am Coll Radiol, 2014, 11(4): 345-351.
6	Livingston DH, Shogan B, John P, et al. CT diagnosis of rib fractures and the prediction of acute respiratory failure[J]. J Trauma, 2008, 64(4): 905-911.
7	Talbot BS, Gange CP Jr, Chaturvedi A, et al. Traumatic rib injury: patterns, imaging pitfalls, complications, and treatment[J]. Radiographics, 2017, 37(2): 628-651.
8	李洪涛. 16层螺旋CT与DR胸片对肋骨骨折的诊断价值分析[J]. 中国继续医学教育, 2016, 8(29): 47-48.
9	Gurcan MN, Sahiner B, Petrick N, et al. Lung nodule detection on thoracic computed tomography images: preliminary evaluation of a computer-aided diagnosis system[J]. Med Phys, 2002, 29(11): 2552-2558.
10	Lehman CD, Blume JD, DeMartini WB, et al. Accuracy and interpretation time of computer-aided detection among novice and experienced breast MRI readers[J]. AJR Am J Roentgenol, 2013, 200(6): W683-W689.
11	Lin FC, Li RY, Tung YW, et al. Morbidity, mortality, associated injuries, and management of traumatic rib fractures[J]. J Chin Med Assoc, 2016, 79(6): 329-334.
12	Dunham CM, Hileman BM, Ransom KJ, et al. Trauma patient adverse outcomes are independently associated with rib cage fracture burden and severity of lung, head, and abdominal injuries[J]. Int J Burns Trauma, 2015, 5(1): 46-55.
13	Barnea Y, Kashtan H, Skornick Y, et al. Isolated rib fractures in elderly patients: mortality and morbidity[J]. Can J Surg, 2002, 45(1): 43-46.
14	Holcomb JB, McMullin NR, Kozar RA, et al. Morbidity from rib fractures increases after age 45[J]. J Am Coll Surg, 2003, 196(4): 549-555.
15	Park S. Clinical analysis for the correlation of intra-abdominal organ injury in the patients with rib fracture[J]. Korean J Thorac Cardiovasc Surg, 2012, 45(4): 246-250.
16	Al-Hassani A, Abdulrahman H, Afifi I, et al. Rib fracture patterns predict thoracic chest wall and abdominal solid organ injury[J]. Am Surg, 2010, 76(8): 888-891.
17	Park HS, Ryu SM, Cho SJ, et al. A treatment case of delayed aortic injury: the patient with posterior rib fracture[J]. Korean J Thorac Cardiovasc Surg, 2014, 47(4): 406-408.
18	Pan Y, Shi D, Wang H, et al. Automatic opportunistic osteoporosis screening using low-dose chest computed tomography scans obtained for lung cancer screening[J]. Eur Radiol, 2020, 30(7): 4107-4116.
19	Ringl H, Lazar M, Töpker M, et al. The ribs unfolded—a CT visualization algorithm for fast detection of rib fractures: effect on sensitivity and specificity in trauma patients[J]. Eur Radiol, 2015, 25(7): 1865-1874.
20	高歌, 马帅, 王霄英. 计算机辅助诊断在医学影像诊断中的基本原理和应用进展[J]. 放射学实践, 2016, 31(12): 1127-1129.
21	高歌, 胡娟, 王成彦, 等. 整合临床及多参数MRI信息的前列腺癌CAD系统: 诊断效能研究[J]. 放射学实践, 2016, 31(12): 1143-1145.
22	韩超, 朱丽娜, 刘想, 等. 基于U-Net实现前列腺MR图像上腺体的自动分割和径线测量: 临床植入验证研究[J]. 放射学实践, 2020, 35(4): 519-524.
23	Liu Y, Liu Q, Han C, et al. The implementation of natural language processing to extract index lesions from breast magnetic resonance imaging reports[J]. BMC Med Inform Decis Mak, 2019, 19(1): 288.

Rib site	True positive /reference standard				False positive	Total detected lesion
Rib site	Displaced RF	Mild RF	Buckle RF	Total	False positive	Total detected lesion
R01	2/2	1/2	0/0	3/4	4	7
R02	5/5	2/4	0/3	7/12	2	9
R03	7/8	5/5	10/12	22/25	5	27
R04	16/16	1/1	16/22	33/39	9	42
R05	15/15	6/8	21/30	42/53	7	49
R06	11/12	3/4	24/27	38/43	5	43
R07	7/9	7/10	21/27	35/46	5	40
R08	8/8	2/5	13/16	23/29	13	36
R09	9/9	5/5	7/10	21/24	9	30
R10	7/7	3/4	4/6	14/17	10	24
R11	3/3	3/3	2/3	8/9	8	16
R12	1/1	0/0	0/0	1/1	4	5
L01	3/3	1/1	1/1	5/5	7	12
L02	3/3	8/10	4/6	15/19	1	16
L03	9/11	2/3	14/21	25/35	6	31
L04	12/12	2/4	14/23	28/39	9	37
L05	10/10	3/4	26/33	39/47	7	46
L06	10/10	7/7	24/26	41/43	10	51
L07	11/11	8/9	19/23	38/43	13	51
L08	8/8	7/7	12/16	27/31	6	33
L09	10/12	6/6	8/10	24/28	6	30
L10	13/14	5/6	3/5	21/25	9	30
L11	3/3	1/3	0/2	4/8	8	12
L12	1/2	0/2	0/1	1/5	11	12

Rib site	True positive /reference standard				False positive	Total detected lesion
Rib site	Displaced RF	Mild RF	Buckle RF	Total	False positive	Total detected lesion
R01	2/2	1/2	0/0	3/4	4	7
R02	5/5	2/4	0/3	7/12	2	9
R03	7/8	5/5	10/12	22/25	5	27
R04	16/16	1/1	16/22	33/39	9	42
R05	15/15	6/8	21/30	42/53	7	49
R06	11/12	3/4	24/27	38/43	5	43
R07	7/9	7/10	21/27	35/46	5	40
R08	8/8	2/5	13/16	23/29	13	36
R09	9/9	5/5	7/10	21/24	9	30
R10	7/7	3/4	4/6	14/17	10	24
R11	3/3	3/3	2/3	8/9	8	16
R12	1/1	0/0	0/0	1/1	4	5
L01	3/3	1/1	1/1	5/5	7	12
L02	3/3	8/10	4/6	15/19	1	16
L03	9/11	2/3	14/21	25/35	6	31
L04	12/12	2/4	14/23	28/39	9	37
L05	10/10	3/4	26/33	39/47	7	46
L06	10/10	7/7	24/26	41/43	10	51
L07	11/11	8/9	19/23	38/43	13	51
L08	8/8	7/7	12/16	27/31	6	33
L09	10/12	6/6	8/10	24/28	6	30
L10	13/14	5/6	3/5	21/25	9	30
L11	3/3	1/3	0/2	4/8	8	12
L12	1/2	0/2	0/1	1/5	11	12

Type	Sensitivity
All the traumatic changes	81.75 （515/630）
Displaced rib fracture	94.85 （184/194）
Mild rib fracture	77.88 （88/113）
Buckle rib fracture	75.23 （243/323）

Type	Sensitivity
All the traumatic changes	81.75 （515/630）
Displaced rib fracture	94.85 （184/194）
Mild rib fracture	77.88 （88/113）
Buckle rib fracture	75.23 （243/323）

Type	Level	SEN/%	SPE/%	PPV/%	NPV/%	PLR	NLR
All the traumatic changes	Patient	90.91	76.21	77.63	90.23	3.82	0.12
	Rib	82.45	98.33	75.30	98.91	49.37	0.18
Displaced rib fracture	Patient	95.56	74.59	52.76	98.26	3.76	0.06
	Rib	94.57	98.26	51.94	99.89	54.35	0.06