Cervical Myelopathy Screening with Machine Learning Algorithm Focusing on Finger Motion Using Noncontact Sensor

Koyama, Tatsuya; Fujita, Koji; Watanabe, Masaru; Kato, Kaho; Sasaki, Tōru; Yoshii, Toshitaka; Nimura, Akimoto; Sugiura, Yuta; Saitō, Hideo; Okawa, Atsushi

doi:10.1097/brs.0000000000004243

Cited by 14 publications

(17 citation statements)

References 54 publications

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“…Jin et al 17 reported the efficacy of their deep-learning algorithm for the analysis of diffusion tensor imaging data in a patient with cervical myelopathy. Koyama et al 18 combined a unique motion sensor with a deep-learning algorithm to detect hand clumsiness due to cervical myelopathy. We also developed a deep-learning algorithm to identify cervical OPLL on radiography, which had a significantly higher diagnostic accuracy than that of experienced spine physicians 19.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning Algorithm for Identifying Cervical Cord Compression Due to Degenerative Canal Stenosis on Radiography

Tamai

Terai

Hoshino

et al. 2023

Spine

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Study design. Cross-sectional study. Objective. Validate the diagnostic accuracy of a deep-learning algorithm for cervical cord compression due to degenerative canal stenosis on radiography. Summary of Background Data. The diagnosis of degenerative cervical myelopathy is often delayed, resulting in improper management. Screening tools for suspected degenerative cervical myelopathy would help identify patients who require detailed physical evaluation. Materials and Methods. Data from 240 patients (120 with cervical stenosis on magnetic resonance imaging and 120 age and sexmatched controls) were randomly divided into training (n = 198) and test (n = 42) data sets. The deep-learning algorithm, designed to identify the suspected stenosis level on radiography, was constructed using a convolutional neural network model called Effi-cientNetB2, and radiography and magnetic resonance imaging data from the training data set. The accuracy and area under the curve of the receiver operating characteristic curve were calculated for the independent test data set. Finally, the number of correct diagnoses was compared between the algorithm and 10 physicians using the test cohort. Results. The diagnostic accuracy and area under the curve of the deep-learning algorithm were 0.81 and 0.81, respectively, in the independent test data set. The rate of correct responses in the test data set was significantly higher for the algorithm than for the physician's consensus (81.0% vs. 66.2%; P = 0.034). Furthermore, the accuracy of the algorithm was greater than that of each individual physician. Conclusions. We developed a deep-learning algorithm capable of suggesting the presence of cervical spinal cord compression on cervical radiography and highlighting the suspected levels on radiographic imaging when cord compression is identified. The diagnostic accuracy of the algorithm was greater than that of spine physicians.

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Section: Discussionmentioning

confidence: 99%

Deep Learning Algorithm for Identifying Cervical Cord Compression Due to Degenerative Canal Stenosis on Radiography

Tamai

Terai

Hoshino

et al. 2023

Spine

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“…In the rule-based method, feature selection for the predetermined features was analyzed using the receiver operating characteristic (ROC) curve, a standard method to assess the performance of binary classification models ( 27 , 28 ). The classification efficacy of features could be assessed by calculating the area under the curve (AUC), which was thought to be poor (< 0.6), fair (0.6–0.7), good (0.7–0.8), very good (0.8–0.9), or excellent (>0.90) ( 29 ).…”

Section: Methodsmentioning

confidence: 99%

Automatic detection of abnormal hand gestures in patients with radial, ulnar, or median nerve injury using hand pose estimation

Fan

Cai

et al. 2022

Front. Neurol.

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BackgroundRadial, ulnar, or median nerve injuries are common peripheral nerve injuries. They usually present specific abnormal signs on the hands as evidence for hand surgeons to diagnose. However, without specialized knowledge, it is difficult for primary healthcare providers to recognize the clinical meaning and the potential nerve injuries through the abnormalities, often leading to misdiagnosis. Developing technologies for automatically detecting abnormal hand gestures would assist general medical service practitioners with an early diagnosis and treatment.MethodsBased on expert experience, we selected three hand gestures with predetermined features and rules as three independent binary classification tasks for abnormal gesture detection. Images from patients with unilateral radial, ulnar, or median nerve injuries and healthy volunteers were obtained using a smartphone. The landmark coordinates were extracted using Google MediaPipe Hands to calculate the features. The receiver operating characteristic curve was employed for feature selection. We compared the performance of rule-based models with logistic regression, support vector machine and of random forest machine learning models by evaluating the accuracy, sensitivity, and specificity.ResultsThe study included 1,344 images, twenty-two patients, and thirty-four volunteers. In rule-based models, eight features were finally selected. The accuracy, sensitivity, and specificity were (1) 98.2, 91.7, and 99.0% for radial nerve injury detection; (2) 97.3, 83.3, and 99.0% for ulnar nerve injury detection; and (3) 96.4, 87.5, and 97.1% for median nerve injury detection, respectively. All machine learning models had accuracy above 95% and sensitivity ranging from 37.5 to 100%.ConclusionOur study provides a helpful tool for detecting abnormal gestures in radial, ulnar, or median nerve injuries with satisfying accuracy, sensitivity, and specificity. It confirms that hand pose estimation could automatically analyze and detect the abnormalities from images of these patients. It has the potential to be a simple and convenient screening method for primary healthcare and telemedicine application.

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“…Cervical myelopathy is a neurological disease caused by age-related degeneration of the cervical spine and ossification of the posterior longitudinal ligament [ 1 , 2 ]. When the disease progresses, numbness in the limbs, dyskinesia, gait disturbance, and vesicorectal disturbance appear [ 3 , 4 ]; however, because the progression is relatively slow, patients are often unaware of their symptoms and are referred to a spine specialist only after the disease has become severe [ 5 , 6 ]. The treatment of severe cervical myelopathy not only requires surgery but also has a worse prognosis than if therapeutic intervention is performed at an early stage [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Development and testing of a new application for measuring motion at the cervical spine

et al. 2022

Self Cite

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Background Cervical myelopathy is a progressive disease, and early detection and treatment contribute to prognosis. Evaluation of cervical intervertebral instability by simple X-ray is used in clinical setting and the information about instability is important to understand the cause of myelopathy, but evaluation of the intervertebral instability by X-ray is complicated. To reduce the burden of clinicians, a system that automatically measures the range of motion was developed by comparing the flexed and extended positions in the lateral view of a simple X-ray of the cervical spine. The accuracy of the system was verified by comparison with spine surgeons and residents to determine whether the system could withstand actual use. Methods An algorithm was created to recognize the four corners of the vertebral bodies in a lateral cervical spine X-ray image, and a system was constructed to automatically measure the range of motion between each vertebra by comparing X-ray images of the cervical spine in extension and flexion. Two experienced spine surgeons and two residents performed the study on the remaining 23 cases. Cervical spine range of motion was measured manually on X-ray images and compared with automatic measurement by this system. Results Of a total of 322 cervical vertebrae in 46 images, 313 (97%) were successfully estimated by our learning model. The mean intersection over union value for all the 46-test data was 0.85. The results of measuring the CRoM angle with the proposed cervical spine motion angle measurement system showed that the mean error from the true value was 3.5° and the standard deviation was 2.8°. The average standard deviations for each measurement by specialist and residents are 2.9° and 3.2°. Conclusions A system for measuring cervical spine range of motion on X-ray images was constructed and showed accuracy comparable to that of spine surgeons. This system will be effective in reducing the burden on and saving time of orthopedic surgeons by avoiding manually measuring X-ray images. Trial registration Retrospectively registered with opt-out agreement.

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Cervical Myelopathy Screening with Machine Learning Algorithm Focusing on Finger Motion Using Noncontact Sensor

Cited by 14 publications

References 54 publications

Deep Learning Algorithm for Identifying Cervical Cord Compression Due to Degenerative Canal Stenosis on Radiography

Deep Learning Algorithm for Identifying Cervical Cord Compression Due to Degenerative Canal Stenosis on Radiography

Automatic detection of abnormal hand gestures in patients with radial, ulnar, or median nerve injury using hand pose estimation

Development and testing of a new application for measuring motion at the cervical spine

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