Screening for degenerative cervical myelopathy with the 10-second grip-and-release test using a smartphone and machine learning: A pilot study

Coin Test: A Complementary Examination for Assessing Upper Extremity Function in Cervical Myelopathy

Study DesignA prospective observational study.ObjectivesTo explore the potential utility of the Coin Test as a valuable tool for assessing and diagnosing cervical spondylotic myelopathy (CSM).MethodsIn the first cohort, 36 patients with balance issues were assessed for CSM using the new Coin Test. In the second cohort, the Coin Test and mJOA scores were compared in 36 CSM patients before and 6 weeks after surgery.ResultsAmong the 36 patients with balance problems who failed tandem gait test, 15 out of 16 (94%) CSM patients failed the Coin Test. The other 20 patients (56%) without CSM completed the Coin Test successfully but failed the tandem gait test for various reasons. The Coin Test demonstrated high specificity (100%) and sensitivity (94%) for diagnosing CSM in patients who failed tandem gait test. In the second cohort, the mJOA score improved significantly from 12 to 15 6 weeks postoperatively, and the Coin Test completion time decreased from 29.5 seconds to 16.4 seconds postoperatively (P < 0.0001). Higher mJOA scores correlate with better performance (shorter time) on the Coin Test, both at baseline and 6 weeks post-surgery.ConclusionThe Coin Test is a useful tool for evaluating hand fine motor and sensory function in CSM patients with high specificity. It also can serve as a tool for assessing surgical outcomes in patients with CSM.

The Application of Artificial Intelligence in Spine Surgery: A Scoping Review

BACKGROUND

A comprehensive review on the application of artificial intelligence (AI) within spine surgery as a specialty remains lacking.

METHODS

This scoping review was conducted upon PubMed and EMBASE databases according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Our analysis focused on publications from January 1, 2020, to March 31, 2024, with a specific focus on AI in the field of spine surgery. Review articles and articles predominantly concerning secondary validation of algorithms, medical physics, electronic devices, biomechanics, preclinical, and with a lack of clinical emphasis were excluded.

RESULTS

One hundred five studies were included after our inclusion/exclusion criteria were applied. Most studies (n = 100) were conducted through supervised learning upon prelabeled data sets. Overall, 38 studies used conventional machine learning methods upon predefined features, whereas 67 used deep learning methods, predominantly for medical image analyses. Only 25.7% of studies (27/105) collected data from more than 1,000 patients for model development and validation. Data originated from only a single center in 72 studies. The most common application was prognostication (38/105), followed by diagnosis (35/105), medical image processing (29/105), and surgical assistance (3/105).

CONCLUSION

The application of AI within the domain of spine surgery has significant potential to advance patient-specific diagnosis, management, and surgical execution.

Optimising early detection of degenerative cervical myelopathy: a systematic review of quantitative screening tools for primary care

Background

Early diagnosis of degenerative cervical myelopathy (DCM) is often challenging due to subtle, non-specific symptoms, limited disease awareness and a lack of definitive diagnostic criteria. As primary care physicians are typically the first to encounter patients with early DCM, equipping them with effective screening tools is crucial for reducing diagnostic delays and improving patient outcomes. This systematic review evaluates the efficacy of quantitative screening methods for DCM that can be implemented in primary care settings.

Methods

A systematic search following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was conducted across PubMed, Embase and Cochrane Library up to July 2024 using keywords relevant to DCM screening. Studies were included if they evaluated the sensitivity and specificity of DCM screening tools applicable to primary care settings. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool.

Results

The search identified 14 studies evaluating 18 screening methods for DCM. Questionnaires consistently showed high diagnostic accuracy, with Youden indices exceeding 0.60, while only three out of nine conventional physical performance tests met the same threshold. Sensor-assisted tests, particularly those using advanced technology like finger-wearable gyro sensors, exhibited the highest diagnostic accuracy but present challenges related to accessibility and learning curves.

Conclusion

This review highlights the potential of quantitative screening methods for early DCM detection in primary care. While questionnaires and conventional tests are effective and accessible, sensor-assisted tests offer greater accuracy but face implementation challenges. A tailored, multifaceted approach is crucial for improving outcomes. Future research should focus on validating these tools in diverse populations and standardising diagnostic criteria.

Diagnosis of carpal tunnel syndrome using a 10-s grip-and-release test with video and machine learning analysis

We developed a finger motion-based diagnostic system for carpal tunnel syndrome by analysing 10 second grip-and-release test videos. Using machine learning, it estimated presence of carpal tunnel syndrome (89% sensitivity and 83% specificity) and correlated with severity on nerve conduction studies (coefficient 0.68).

LEVEL OF EVIDENCE

III.

Deep Learning-Enhanced Hand Grip and Release Test for Degenerative Cervical Myelopathy: Shortening Assessment Duration to 6 Seconds

OBJECTIVE

Hand clumsiness and reduced hand dexterity can signal early signs of degenerative cervical myelopathy (DCM). While the 10-second grip and release (10-s G&R) test is a common clinical tool for evaluating hand function, a more accessible method is warranted. This study explores the use of deep learning-enhanced hand grip and release test (DL-HGRT) for predicting DCM and evaluates its capability to reduce the duration of the 10-s G&R test.

METHODS

The retrospective study included 508 DCM patients and 1,194 control subjects. Propensity score matching (PSM) was utilized to minimize the confounding effects related to age and sex. Videos of the 10-s G&R test were captured using a smartphone application. The 3D-MobileNetV2 was utilized for analysis, generating a series of parameters. Additionally, receiver operating characteristic curves were employed to assess the performance of the 10-s G&R test in predicting DCM and to evaluate the effectiveness of a shortened testing duration.

RESULTS

Patients with DCM exhibited impairments in most 10-s G&R test parameters. Before PSM, the number of cycles achieved the best diagnostic performance (area under the curve [AUC], 0.85; sensitivity, 80.12%; specificity, 74.29% at 20 cycles), followed by average grip time. Following PSM for age and gender, the AUC remained above 0.80. The average grip time achieved the highest AUC of 0.83 after 6 seconds, plateauing with no significant improvement in extending the duration to 10 seconds, indicating that 6 seconds is an adequate timeframe to efficiently evaluate hand motor dysfunction in DCM based on DL-HGRT.

CONCLUSION

DL-HGRT demonstrates potential as a promising supplementary tool for predicting DCM. Notably, a testing duration of 6 seconds appears to be sufficient for accurate assessment, enhancing the test more feasible and practical without compromising diagnostic performance.