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Journal of Peking University (Health Sciences) ›› 2023, Vol. 55 ›› Issue (4): 670-675. doi: 10.19723/j.issn.1671-167X.2023.04.017

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Value of artificial intelligence in the improvement of diagnostic consistency of radiology residents

Xiang LIU1,Hui-hui XIE1,Yu-feng XU1,Xiao-dong ZHANG1,Xiao-feng TAO2,Lin LIU3,Xiao-ying WANG1,*()   

  1. 1. Department of Radiology, Peking University First Hospital, Beijing 100034, China
    2. Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200011, China
    3. Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun 130000, China
  • Received:2020-11-12 Online:2023-08-18 Published:2023-08-03
  • Contact: Xiao-ying WANG E-mail:wangxiaoying@bjmu.edu.cn

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Abstract:

Objective: To explore the value of artificial intelligence (AI) in improving the detection rate of traumatic rib fractures by radiologist residents and the consistency among different readers. Methods: Chest CT images of 393 patients with acute trauma from China-Japan Union Hospital of Jilin University (hospital 02) and Shanghai Ninth People' s Hospital (hospital 03) were collected in this research. The consensus achieved by three radiology experts was regarded as the reference standard. All the images assigned to three hospitals: Peking University First Hospital (hospital 01), hospital 02 and hospital 03, and were then randomly divided into two groups (group A and group B: group A included 197 patients, and group B included 196 patients). Each group was read by one radiologist resident from each hospital for rib fracture detection. Each case was read twice by the same radiologist, with and without the assistance of the AI ["radiologist-only" reading and "radiologist + AI" reading]. The detection rates of different types of rib fractures (displaced fractures and occult fractures) were compared between "radiologist-only" reading and "radiologist + AI" reading. The consistencies of different radiologists with different reading methods were evaluated. Results: The detection rates of displaced rib fractures and occult rib fractures by "radiologist + AI" reading were significantly higher than those read by "radiologist-only" reading (94.56% vs. 78.40%, 76.60% vs. 49.42%, P < 0.001). For "radiologist-only reading", the Kappa coefficients of the radiologists between hospital 01 and hospital 03 were slightly greater than 0.4 (indicating moderate consistency), the coefficients of the radiologists between hospital 01/hospital 02 and hospital 02/hospital 03 were less than 0.4 (indicating poor consistency). The Phi coefficients of the radiologists among different hospitals were all less than 0.6 (indicating moderate correlation). With "radiologist + AI" reading, the Kappa and Phi coefficient among the radiologists in dif-ferent hospitals were greater than or equal to 0.6 (indicating good consistency and correlation). Conclusion: AI software can be used to automatically detect suspected rib fracture lesions, which helps to improve the detection rate of fracture lesions and the consistency among different readers.

Key words: Rib fracture, Computed tomography imaging, Chest trauma, Artificial intelligence

CLC Number: 

  • R814.4

Table 1

The typical parameters of five CT scanning"

Parameters Brilliance 64 Brilliance 256 Lightspeed 16 NeuViz 128 Aquilion ONE
Scan length/mm 500 500 500 500 400-500
Tube voltage/kV 120 120 120 120 120
Tube current/mA 345 194-492 210 273-580 281-440
Slice thickness/mm 1 1 1.25 1 1
Image matrix 512×512 512×512 512×512 512×512 512×512
Kernal Bone Bone Bone Bone Bone

Table 2

Detection rates of different types of rib fractures"

Types of rib fractures Index Radiologist-only, n (%) Radiologist + AI, n (%) P value
Displaced fracture Detected 461(78.40) 556(94.56) < 0.001
Not detected 127(21.60) 32(5.44)
Total 588(100.00) 588(100.00)
Occult fracture Detected 642(49.42) 995(76.60) < 0.001
Not detected 657(50.58) 304(23.40)
Total 1 299(100.00) 1 299(100.00)
All fractures Detected 1 103(58.45) 1 551(82.19) < 0.001
Not detected 784(41.55) 336(17.81)
Total 1 887(100.00) 1 887(100.00)

Figure 1

A 65-year-old male patient with displaced fractures on the right 5th and 8th rib A, displaced fractures on the right 5th and 8th rib by reference standard reading (as shown in pink box); B, two rib fractures were detected by "radio-logist + AI" reading (as shown in yellow box); C, two rib fractures were detected by "radiologist-only" reading as well (as shown in orange box). AI, artificial intelligence."

Figure 2

A 60-year-old male patient with occult fracture on the left 6th rib A, occult fracture on the left 6th rib by reference standard reading (as shown in pink box); B, rib fracture was detected by "radiologist + AI" reading (as shown in yellow box); C, rib fracture was not detected by "radiologist-only" reading. AI, artificial intelligence."

Table 3

Consistency among different "radiologists-only" reading"

Items χ2 P value Phi coefficient Degree of correlation Kappa (95%CI) Degree of consistency
A1-A2 38.586 < 0.001 0.402 Moderate 0.371 (0.250, 0.493) Poor
A1-A3 61.843 < 0.001 0.509 Moderate 0.491 (0.366, 0.616) Moderate
A2-A3 23.119 < 0.001 0.311 Moderate 0.308 (0.182, 0.435) Poor
B1-B2 52.185 < 0.001 0.366 Moderate 0.365 (0.273, 0.457) Poor
B1-B3 67.168 < 0.001 0.415 Moderate 0.415 (0.325, 0.505) Moderate
B2-B3 58.210 < 0.001 0.386 Moderate 0.385 (0.294, 0.477) Poor

Table 4

The consistency among different "radiologists + AI" reading"

Items χ2 P value Phi coefficient Degree of correlation Kappa (95%CI) Degree of consistency
A1-A2 68.209 < 0.001 0.618 Good 0.601 (0.458, 0.744) Good
A1-A3 85.393 < 0.001 0.707 Good 0.698 (0.564, 0.832) Good
A2-A3 102.028 < 0.001 0.653 Good 0.652 (0.520, 0.783) Good
B1-B2 144.135 < 0.001 0.608 Good 0.596 (0.497, 0.695) Good
B1-B3 177.701 < 0.001 0.675 Good 0.668 (0.577, 0.759) Good
B2-B3 232.594 < 0.001 0.772 Good 0.771 (0.687, 0.855) Excellent
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