基于深度学习的人工智能软件在肋骨新鲜骨折CT诊断中的应用价值

doi:10.3969/j.issn.1671-7171.2024.10.033

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Abstract 【Objective】 To explore the application value of deep learning-based artificial intelligence (AI) software in the diagnosis of CT of fresh rib fractures.【Methods】A total of 259 qualified fresh fracture patients admitted to our hospital from June 2022 to September 2023 were selected and their imaging data were introduced to the AI workstation; Based on a 8-year attending physician in the department of radiology and AI software to diagnose rib fractures, the group A was diagnosed separately by AI software, and the group B and the group AB were diagnosed by physician combined with AI software. The number, site, and classification of fracture lesions were recorded in the three groups; The results of the three groups were analyzed with the reference standard to compare the sensitivity, misdiagnosis rate and detection rate of incomplete fractures in the three groups; Mean time of diagnosis was compared between the groups B and the group AB. 【Results】There were 563 fractures in 259 patients; There were 385 complete fractures and 178 incomplete fractures. The sensitivity of fracture detection in the group A, the group B and the group AB was 91.47%, 83.13%, and 92.54%, respectively. The sensitivity comparison of the three groups showed statistically significant differences (P<0.05).The sensitivity of fractures in the group A and the group AB was higher than that in the group B, significant (P<0.05). The misdiagnosis rates in the group A, the group B and the group AB were 20.03%, 13.17% and 12.58%, respectively, which was statistically significant (P<0.05).The group A had the highest misdiagnosis rate, significantly higher than that of the group AB and the group B, with a statistically significant difference (all P<0.05). The detection rate of incomplete fractures in the group A, the group B and the group AB was 75.28%, 50.56% and 76.40%, respectively, and the difference was statistically significant (P<0.05).The mean diagnosis time in the group AB was shorter than that of the group B , with significant difference(P<0.05).【Conclusion】The joint diagnosis between doctors and AI improves the sensitivity of the detection of fresh rib fractures, which is mainly reflected in the detection rate of incomplete fractures, and can also significantly shorten the diagnosis time and improve the work efficiency.

Key words： Rib Fractures/DG Tomography, Spiral Computed Artificial Intelligence

Received: 10 January 2024

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http://journal07.magtech.org.cn/yxlcyj/EN/10.3969/j.issn.1671-7171.2024.10.033 OR http://journal07.magtech.org.cn/yxlcyj/EN/Y2024/V41/I10/1571

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