臨床整形外科57巻10号

2022年10月発行

特集 整形外科領域における人工知能の応用 各論

人工知能を活用した骨粗鬆症性椎体骨折の画像診断補助システム—MR画像における新鮮椎体骨折の識別

著者： 藪晋人¹ 星野雅俊² 田淵仁志³⁴ 高橋真治¹ 寺井秀富¹ 升本浩紀³ 前野考史⁵ 岩前真由¹ 豊田宏光¹ 鈴木亨暢¹ 玉井孝司¹ 猪瀬弘之⁶ 吉井俊貴⁶ 大川淳⁶ 中村博亮¹

所属機関： ¹大阪公立大学大学院医学研究科整形外科学 ²大阪市立総合医療センター整形外科 ³ツカザキ病院眼科 ⁴広島大学医学部医療のためのテクノロジーとデザインシンキング寄附講座 ⁵石切生喜病院整形外科 ⁶東京医科歯科大学整形外科

ページ範囲：P.1205 - P.1211

畳み込みニューラルネットワーク(CNN)を用いてMR画像(T1強調画像，矢状断)による新鮮骨粗鬆症性椎体骨折(OVF)の診断を行い，その精度を2人の脊椎外科医と比較した．新鮮OVFの診断においてCNNのROC曲線下面積は0.949であった．感度，特異性および精度は(CNN/脊椎外科医1/脊椎外科医2)感度：0.881/0.881/1.000，特異度：0.879/0.862/0.655，精度：0.880/0.870/0.800であった．MR画像を用いた新鮮OVF診断において，CNN分類器の診断能は良好であり，また脊椎外科医と同等であった．

参考文献

1) Yabu A, Hoshino M, Tabuchi H, et al. Using artificial intelligence to diagnose fresh osteoporotic vertebral fractures on magnetic resonance images. Spine J 2021;21(10):1652-8.

2) Selvaraju RR, Cogswell M, Das A, et al. Grad-cam:visual explanations from deep networks via gradient-based localization. Proc IEEE Int Conf Comput Vis 2017;618-26.

3) 藪　晋人，高橋真治，寺井秀富・他．人工知能技術を活用した骨粗鬆症性椎体骨折の画像診断補助システム．J Spine Res 2022；13：844-50．

4) Liu X, Faes L, Kale AU, et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging:a systematic review and meta-analysis. Lancet Digit Health 2019;1(6):e271-97.

5) Duron L, Ducarouge A, Gillibert A, et al. Assessment of an AI aid in detection of adult appendicular skeletal fractures by emergency physicians and radiologists:a multicenter cross-sectional diagnostic study. Radiology 2021;300(1):120-9.

6) Delmas PD, van de Langerijt L, Watts NB, et al. Underdiagnosis of vertebral fractures is a worldwide problem:the IMPACT study. J Bone Miner Res 2005;20(4):557-63.

7) Kanchiku T, Taguchi T, Kawai S. Magnetic resonance imaging diagnosis and new classification of the osteoporotic vertebral fracture. J Orthop Sci 2003;8(4):463-6.

8) Chen HY, Hsu BW, Yin YK, et al. Application of deep learning algorithm to detect and visualize vertebral fractures on plain frontal radiographs. PLoS One 2021;16(1):e0245992. doi:10.1371/journal.pone.0245992.

9) Murata K, Endo K, Aihara T, et al. Artificial intelligence for the detection of vertebral fractures on plain spinal radiography. Sci Rep 2020;10(1):1-8. doi:10.1038/s41598-020-76866-w.

10) Li YC, Chen HH, Horng-Shing Lu H, et al. Can a deep-learning model for the automated detection of vertebral fractures approach the performance level of human subspecialists? Clin Orthop Relat Res 2021;479(7):1598-612.

11) Tomita N, Cheung YY, Hassanpour S. Deep neural networks for automatic detection of osteoporotic vertebral fractures on CT scans. Comput Biol Med 2018;98:8-15.

12) Bar A, Wolf L, Amitai OB, et al:Compression fractures detection on CT. Proc. SPIE 10134, Medical imaging 2017:Computer-Aided Diagnosis 2017;1013440:1036-43.

13) Sadineni RT, Pasumarthy A, Bellapa NC, et al. Imaging patterns in MRI in recent bone injuries following negative or inconclusive plain radiographs. J Clin Diagn Res 2015;9(10):TC10-3.

14) Zhang Y, Qi H, Zhang Y, et al. Vertebral bone marrow edema in magnetic resonance imaging correlates with bone healing histomorphometry in (sub) acute osteoporotic vertebral compression fracture. Eur Spine J 2021;30(9):2708-17.

15) Takahashi S, Hoshino M, Takayama K, et al. Time course of osteoporotic vertebral fractures by magnetic resonance imaging using a simple classification:a multicenter prospective cohort study. Osteoporos Int 2017;28(2):473-82.

16) Wu E, Wu K, Daneshjou R, et al. How medical AI devices are evaluated:limitations and recommendations from an analysis of FDA approvals. Nat Med 2021;27(4):582-4.

17) Kolanu N, Silverstone EJ, Ho BH, et al. Clinical utility of computer-aided diagnosis of vertebral fractures from computed tomography images. J Bone Miner Res 2020;35(12):2307-12.

18) Kijowski R, Liu F, Caliva F, et al. Deep learning for lesion detection, progression, and prediction of musculoskeletal disease. J Magn Reson Imaging 2020;52(6):1607-19.

19) Takahashi S, Hoshino M, Takayama K, et al. Predicting delayed union in osteoporotic vertebral fractures with onsecutive magnetic resonance imaging in the acute phase:a multicenter cohort study. Osteoporos Int 2016;27(12):3567-75.

20) Wen YL, Leech M. Review of the role of radiomics in tumour risk classification and prognosis of cancer. Anticancer Res 2020;40(7):3605-18.

21) Muehlematter UJ, Mannil M, Becker AS, et al. Vertebral body insufficiency fractures:detection of vertebrae at risk on standard CT images using texture analysis and machine learning. Eur Radiol 2019;29(5):2207-17.