电邮这篇文章 用脊柱正面 X 光片自动诊断脊柱侧弯的新型智能系统：准确度、优势和局限性
- 作者: Kassab D.K.1, Kamyshanskaya I.G.1, Trukhan S.V.2
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隶属关系:
- Saint Petersburg State University
- Esper LLC
- 期: 卷 5, 编号 2 (2024)
- 页面: 243-254
- 栏目: 原创性科研成果
- URL: https://journal-vniispk.ru/DD/article/view/264836
- DOI: https://doi.org/10.17816/DD630093
- ID: 264836
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论证。脊柱侧弯是最常见的脊柱畸形之一，通常使用 Cobb 方法正面 X 光片进行诊断。基于人工智能的自动测量方法弥补了标准方法的许多不足，可以大大节省放射科医生的时间。
目的是分析一种新的人工智能程序在通过自动测量正面 X 光片上的 Cobb 角来评估脊柱侧弯程度方面的准确度和优缺点。
材料和方法。共检查了 114 张 X 光片，以确定人工智能软件自动测量的 Cobb 角与放射科医生使用 Microsoft Excel 中的 Bland–Altman 方法测量的 Cobb 角是否一致。此外，还使用有限的数据（120 张 X 光片）进行了临床准确度测试。通过灵敏度、特异性、准确度和 ROC 曲线下面积评估了该系统在确定脊柱侧弯严重程度方面的准确度。
结果。I度和II度脊柱侧弯组中，人工智能程序和放射科医生计算出的 Cobb 角测量值更加一致。只有 2.8% 的病例在 Cobb 角测量值上存在显著的临床差异（差异大于 5°）。在 Mariinsky City Hospital（圣彼得堡）进行的有限临床试验中获得的诊断准确度值也证实了该系统的可靠性：灵敏度为 0.97，特异性为 0.88，准确度（总体有效性）为 0.93，ROC 曲线下的面积为 0.93。
结论。一般来说，人工智能程序可以自动准确地确定脊柱侧弯的严重程度，并利用正面 X 光片测量脊柱弯曲的角度。
作者简介
Dima Kh. I. Kassab
Saint Petersburg State University
编辑信件的主要联系方式.
Email: DimaKK87@gmail.com
ORCID iD: 0000-0001-5085-6614
SPIN 代码: 4907-7850
MD
俄罗斯联邦, Saint PetersburgIrina G. Kamyshanskaya
Saint Petersburg State University
Email: irinaka@mail.ru
ORCID iD: 0000-0002-8351-9216
SPIN 代码: 2422-5191
MD, Dr. Sci. (Medicine), Assistant Professor
俄罗斯联邦, Saint PetersburgStanislau V. Trukhan
Esper LLC
Email: stas.truhan@gmail.com
ORCID iD: 0000-0003-0688-0988
俄罗斯联邦, Tver
参考
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Fig. 1. Results of measurements by the Blend-Altman method. Agreement of Cobb angle measurements by two methods on radiographs with scoliosis of grades I and II.
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Fig. 2. Results of measurements by the Blend-Altman method. Agreement of Cobb angle measurements by two methods on radiographs with scoliosis of grades III and IV.
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Fig. 3. ROC curve confirmation of the accuracy of the new AI program in determining the degree of scoliosis.
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Fig. 4. X-ray image of grade II scoliosis. The image was analyzed by the AI program (left) and a radiologist (right). The radiologist did not measure the thoracolumbar curve, since the lower end vertebra is not visible in the image.
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Fig. 5. Errors in marking the vertebrae on X-ray images with a grade of 0 (normal). In images of the chest organs, unclear visualization of the vertebral boundaries can lead to false measurement results.
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Fig. 6. Errors in marking the vertebrae on X-ray images with a grade of 0 (normal). Errors in determining the body of the vertebra L V can lead to false positive results (left).
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Fig. 7. Program errors in evaluating images with scoliosis of grade 0. Cobb angles measured by a radiologist (left) and an artificial intelligence program (right). The variability of measurements is insignificant (1.4°), but the doctor determines scoliosis grade 0, and artificial intelligence - grade I.
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Fig. 8. Distribution of Cobb angles measured by artificial intelligence on normal X-ray images (in the group of X-ray images without scoliosis).
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Fig. 9. Features of vertebral marking on X-ray images for scoliosis of grades III and IV. Errors in determining and numbering the vertebrae due to their atypical shape (yellow arrow).
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Fig. 10. Scoliosis of grade III diagnosed by a radiologist (left) and artificial intelligence (right). Significant variability in measuring the lumbar curvature (7.8°) did not affect the overall assessment of scoliosis.
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