Yejin Jeon, Bo Ram Kim, Hyoung In Choi, Eugene Lee, Da-Wit Kim, Boorym Choi, Joon Woo Lee
{"title":"利用腹部 CT 诊断腰椎中央管狭窄的深度学习算法的可行性。","authors":"Yejin Jeon, Bo Ram Kim, Hyoung In Choi, Eugene Lee, Da-Wit Kim, Boorym Choi, Joon Woo Lee","doi":"10.1007/s00256-024-04796-z","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>To develop a deep learning algorithm for diagnosing lumbar central canal stenosis (LCCS) using abdominal CT (ACT) and lumbar spine CT (LCT).</p><p><strong>Materials and methods: </strong>This retrospective study involved 109 patients undergoing LCTs and ACTs between January 2014 and July 2021. The dural sac on CT images was manually segmented and classified as normal or stenosed (dural sac cross-sectional area ≥ 100 mm<sup>2</sup> or < 100 mm<sup>2</sup>, respectively). A deep learning model based on U-Net architecture was developed to automatically segment the dural sac and classify the central canal stenosis. The classification performance of the model was compared on a testing set (990 images from 9 patients). The accuracy, sensitivity, and specificity of automatic segmentation were quantitatively evaluated by comparing its Dice similarity coefficient (DSC) and intraclass correlation coefficient (ICC) with those of manual segmentation.</p><p><strong>Results: </strong>In total, 990 CT images from nine patients (mean age ± standard deviation, 77 ± 7 years; six men) were evaluated. The algorithm achieved high segmentation performance with a DSC of 0.85 ± 0.10 and ICC of 0.82 (95% confidence interval [CI]: 0.80,0.85). The ICC between ACTs and LCTs on the deep learning algorithm was 0.89 (95%CI: 0.87,0.91). The accuracy of the algorithm in diagnosing LCCS with dichotomous classification was 84%(95%CI: 0.82,0.86). In dataset analysis, the accuracy of ACTs and LCTs was 85%(95%CI: 0.82,0.88) and 83%(95%CI: 0.79,0.86), respectively. The model showed better accuracy for ACT than LCT.</p><p><strong>Conclusion: </strong>The deep learning algorithm automatically diagnosed LCCS on LCTs and ACTs. ACT had a diagnostic performance for LCCS comparable to that of LCT.</p>","PeriodicalId":21783,"journal":{"name":"Skeletal Radiology","volume":" ","pages":"947-957"},"PeriodicalIF":1.9000,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11953181/pdf/","citationCount":"0","resultStr":"{\"title\":\"Feasibility of deep learning algorithm in diagnosing lumbar central canal stenosis using abdominal CT.\",\"authors\":\"Yejin Jeon, Bo Ram Kim, Hyoung In Choi, Eugene Lee, Da-Wit Kim, Boorym Choi, Joon Woo Lee\",\"doi\":\"10.1007/s00256-024-04796-z\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>To develop a deep learning algorithm for diagnosing lumbar central canal stenosis (LCCS) using abdominal CT (ACT) and lumbar spine CT (LCT).</p><p><strong>Materials and methods: </strong>This retrospective study involved 109 patients undergoing LCTs and ACTs between January 2014 and July 2021. The dural sac on CT images was manually segmented and classified as normal or stenosed (dural sac cross-sectional area ≥ 100 mm<sup>2</sup> or < 100 mm<sup>2</sup>, respectively). A deep learning model based on U-Net architecture was developed to automatically segment the dural sac and classify the central canal stenosis. The classification performance of the model was compared on a testing set (990 images from 9 patients). The accuracy, sensitivity, and specificity of automatic segmentation were quantitatively evaluated by comparing its Dice similarity coefficient (DSC) and intraclass correlation coefficient (ICC) with those of manual segmentation.</p><p><strong>Results: </strong>In total, 990 CT images from nine patients (mean age ± standard deviation, 77 ± 7 years; six men) were evaluated. The algorithm achieved high segmentation performance with a DSC of 0.85 ± 0.10 and ICC of 0.82 (95% confidence interval [CI]: 0.80,0.85). The ICC between ACTs and LCTs on the deep learning algorithm was 0.89 (95%CI: 0.87,0.91). The accuracy of the algorithm in diagnosing LCCS with dichotomous classification was 84%(95%CI: 0.82,0.86). In dataset analysis, the accuracy of ACTs and LCTs was 85%(95%CI: 0.82,0.88) and 83%(95%CI: 0.79,0.86), respectively. The model showed better accuracy for ACT than LCT.</p><p><strong>Conclusion: </strong>The deep learning algorithm automatically diagnosed LCCS on LCTs and ACTs. ACT had a diagnostic performance for LCCS comparable to that of LCT.</p>\",\"PeriodicalId\":21783,\"journal\":{\"name\":\"Skeletal Radiology\",\"volume\":\" \",\"pages\":\"947-957\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2025-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11953181/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Skeletal Radiology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s00256-024-04796-z\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/9/9 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"ORTHOPEDICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Skeletal Radiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s00256-024-04796-z","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/9/9 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"ORTHOPEDICS","Score":null,"Total":0}
Feasibility of deep learning algorithm in diagnosing lumbar central canal stenosis using abdominal CT.
Objective: To develop a deep learning algorithm for diagnosing lumbar central canal stenosis (LCCS) using abdominal CT (ACT) and lumbar spine CT (LCT).
Materials and methods: This retrospective study involved 109 patients undergoing LCTs and ACTs between January 2014 and July 2021. The dural sac on CT images was manually segmented and classified as normal or stenosed (dural sac cross-sectional area ≥ 100 mm2 or < 100 mm2, respectively). A deep learning model based on U-Net architecture was developed to automatically segment the dural sac and classify the central canal stenosis. The classification performance of the model was compared on a testing set (990 images from 9 patients). The accuracy, sensitivity, and specificity of automatic segmentation were quantitatively evaluated by comparing its Dice similarity coefficient (DSC) and intraclass correlation coefficient (ICC) with those of manual segmentation.
Results: In total, 990 CT images from nine patients (mean age ± standard deviation, 77 ± 7 years; six men) were evaluated. The algorithm achieved high segmentation performance with a DSC of 0.85 ± 0.10 and ICC of 0.82 (95% confidence interval [CI]: 0.80,0.85). The ICC between ACTs and LCTs on the deep learning algorithm was 0.89 (95%CI: 0.87,0.91). The accuracy of the algorithm in diagnosing LCCS with dichotomous classification was 84%(95%CI: 0.82,0.86). In dataset analysis, the accuracy of ACTs and LCTs was 85%(95%CI: 0.82,0.88) and 83%(95%CI: 0.79,0.86), respectively. The model showed better accuracy for ACT than LCT.
Conclusion: The deep learning algorithm automatically diagnosed LCCS on LCTs and ACTs. ACT had a diagnostic performance for LCCS comparable to that of LCT.
期刊介绍:
Skeletal Radiology provides a forum for the dissemination of current knowledge and information dealing with disorders of the musculoskeletal system including the spine. While emphasizing the radiological aspects of the many varied skeletal abnormalities, the journal also adopts an interdisciplinary approach, reflecting the membership of the International Skeletal Society. Thus, the anatomical, pathological, physiological, clinical, metabolic and epidemiological aspects of the many entities affecting the skeleton receive appropriate consideration.
This is the Journal of the International Skeletal Society and the Official Journal of the Society of Skeletal Radiology and the Australasian Musculoskelelal Imaging Group.