Feasibility of artificial intelligence assisted quantitative muscle ultrasound in carpal tunnel syndrome
Abstract Background In case of focal neuropathy, the muscle fibers innervated by the corresponding nerves are replaced with fat or fibrous tissue due to denervation, which results in increased echo intensity (EI) on ultrasonography. EI analysis can be conducted quantitatively using gray scale analys...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-06-01
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| Series: | BMC Musculoskeletal Disorders |
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| Online Access: | https://doi.org/10.1186/s12891-023-06623-3 |
| _version_ | 1797789973652439040 |
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| author | Sun Woong Kim Sunwoo Kim Dongik Shin Jae Hyeong Choi Jung Sub Sim Seungjun Baek Joon Shik Yoon |
| author_facet | Sun Woong Kim Sunwoo Kim Dongik Shin Jae Hyeong Choi Jung Sub Sim Seungjun Baek Joon Shik Yoon |
| author_sort | Sun Woong Kim |
| collection | DOAJ |
| description | Abstract Background In case of focal neuropathy, the muscle fibers innervated by the corresponding nerves are replaced with fat or fibrous tissue due to denervation, which results in increased echo intensity (EI) on ultrasonography. EI analysis can be conducted quantitatively using gray scale analysis. Mean value of pixel brightness of muscle image defined as EI. However, the accuracy achieved by using this parameter alone to differentiate between normal and abnormal muscles is limited. Recently, attempts have been made to increase the accuracy using artificial intelligence (AI) in the analysis of muscle ultrasound images. CTS is the most common disease among focal neuropathy. In this study, we aimed to verify the utility of AI assisted quantitative analysis of muscle ultrasound in CTS. Methods This is retrospective study that used data from adult who underwent ultrasonographic examination of hand muscles. The patient with CTS confirmed by electromyography and subjects without CTS were included. Ultrasound images of the unaffected hands of patients or subjects without CTS were used as controls. Ultrasonography was performed by one physician in same sonographic settings. Both conventional quantitative grayscale analysis and machine learning (ML) analysis were performed for comparison. Results A total of 47 hands with CTS and 27 control hands were analyzed. On conventional quantitative analysis, mean EI ratio (i.e. mean thenar EI/mean hypothenar EI ratio) were significantly higher in the patient group than in the control group, and the AUC was 0.76 in ROC analysis. In the analysis using machine learning, the AUC was the highest for the linear support vector classifier (AUC = 0.86). When recursive feature elimination was applied to the classifier, the AUC value improved to 0.89. Conclusion This study showed a significant increase in diagnostic accuracy when AI was used for quantitative analysis of muscle ultrasonography. If an analysis protocol using machine learning can be established and mounted on an ultrasound machine, a noninvasive and non-time-consuming muscle ultrasound examination can be conducted as an ancillary tool for diagnosis. |
| first_indexed | 2024-03-13T01:58:10Z |
| format | Article |
| id | doaj.art-385284e76acc483b8f814107b22a4f9a |
| institution | Directory Open Access Journal |
| issn | 1471-2474 |
| language | English |
| last_indexed | 2024-03-13T01:58:10Z |
| publishDate | 2023-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Musculoskeletal Disorders |
| spelling | doaj.art-385284e76acc483b8f814107b22a4f9a2023-07-02T11:03:38ZengBMCBMC Musculoskeletal Disorders1471-24742023-06-012411810.1186/s12891-023-06623-3Feasibility of artificial intelligence assisted quantitative muscle ultrasound in carpal tunnel syndromeSun Woong Kim0Sunwoo Kim1Dongik Shin2Jae Hyeong Choi3Jung Sub Sim4Seungjun Baek5Joon Shik Yoon6Department of Physical and Rehabilitation Medicine, Korea University Guro HospitalDepartment of Computer Science and Engineering, Korea UniversityDepartment of Computer Science and Engineering, Korea UniversityDepartment of Physical and Rehabilitation Medicine, Korea University Guro HospitalDepartment of Computer Science and Engineering, Korea UniversityDepartment of Computer Science and Engineering, Korea UniversityDepartment of Physical and Rehabilitation Medicine, Korea University Guro HospitalAbstract Background In case of focal neuropathy, the muscle fibers innervated by the corresponding nerves are replaced with fat or fibrous tissue due to denervation, which results in increased echo intensity (EI) on ultrasonography. EI analysis can be conducted quantitatively using gray scale analysis. Mean value of pixel brightness of muscle image defined as EI. However, the accuracy achieved by using this parameter alone to differentiate between normal and abnormal muscles is limited. Recently, attempts have been made to increase the accuracy using artificial intelligence (AI) in the analysis of muscle ultrasound images. CTS is the most common disease among focal neuropathy. In this study, we aimed to verify the utility of AI assisted quantitative analysis of muscle ultrasound in CTS. Methods This is retrospective study that used data from adult who underwent ultrasonographic examination of hand muscles. The patient with CTS confirmed by electromyography and subjects without CTS were included. Ultrasound images of the unaffected hands of patients or subjects without CTS were used as controls. Ultrasonography was performed by one physician in same sonographic settings. Both conventional quantitative grayscale analysis and machine learning (ML) analysis were performed for comparison. Results A total of 47 hands with CTS and 27 control hands were analyzed. On conventional quantitative analysis, mean EI ratio (i.e. mean thenar EI/mean hypothenar EI ratio) were significantly higher in the patient group than in the control group, and the AUC was 0.76 in ROC analysis. In the analysis using machine learning, the AUC was the highest for the linear support vector classifier (AUC = 0.86). When recursive feature elimination was applied to the classifier, the AUC value improved to 0.89. Conclusion This study showed a significant increase in diagnostic accuracy when AI was used for quantitative analysis of muscle ultrasonography. If an analysis protocol using machine learning can be established and mounted on an ultrasound machine, a noninvasive and non-time-consuming muscle ultrasound examination can be conducted as an ancillary tool for diagnosis.https://doi.org/10.1186/s12891-023-06623-3Muscle ultrasoundQuantitative ultrasoundCarpal tunnel syndromeMachine learningArtificial intelligence |
| spellingShingle | Sun Woong Kim Sunwoo Kim Dongik Shin Jae Hyeong Choi Jung Sub Sim Seungjun Baek Joon Shik Yoon Feasibility of artificial intelligence assisted quantitative muscle ultrasound in carpal tunnel syndrome BMC Musculoskeletal Disorders Muscle ultrasound Quantitative ultrasound Carpal tunnel syndrome Machine learning Artificial intelligence |
| title | Feasibility of artificial intelligence assisted quantitative muscle ultrasound in carpal tunnel syndrome |
| title_full | Feasibility of artificial intelligence assisted quantitative muscle ultrasound in carpal tunnel syndrome |
| title_fullStr | Feasibility of artificial intelligence assisted quantitative muscle ultrasound in carpal tunnel syndrome |
| title_full_unstemmed | Feasibility of artificial intelligence assisted quantitative muscle ultrasound in carpal tunnel syndrome |
| title_short | Feasibility of artificial intelligence assisted quantitative muscle ultrasound in carpal tunnel syndrome |
| title_sort | feasibility of artificial intelligence assisted quantitative muscle ultrasound in carpal tunnel syndrome |
| topic | Muscle ultrasound Quantitative ultrasound Carpal tunnel syndrome Machine learning Artificial intelligence |
| url | https://doi.org/10.1186/s12891-023-06623-3 |
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