Quantitative assessment of hand motor function in cervical spinal disorder patients using target tracking tests

A Pilot Study of a Finger Kinematic Parameter-Based Tool for Evaluating Degenerative Cervical Myelopathy

STUDY DESIGN

This is a cross-sectional study.

OBJECTIVE

To evaluate the effectiveness of a novel finger Kinematic Parameter-Based Tool in the grip and release (G&R) test for assessing degenerative cervical myelopathy (DCM).

SUMMARY OF BACKGROUND DATA

The development and progression of DCM symptoms are gradual and obscure. Although previous studies have objectively evaluated hand movements specific to myelopathy using the G&R test, virtual reality, or wearable sensors, these methods have limitations, such as limited discrimination or inconvenience for simple screening. Consequently, there is a need to develop effective screening methods.

MATERIALS AND METHODS

Totally, 297 asymptomatic volunteers and 258 DCM patients were enrolled. This system comprises a wearable acceleration/gyro sensor. The acceleration/gyro sensor was placed on the little finger of the participants to perform 40 cycles of full-range G&R as quickly as possible. The collected data were then transformed into kinematic parameters using sensor-based software and R studio software (version: RStudio 2022.07.2+576, Boston, USA). Gender, age, and body mass index (BMI) subgroups (classified as BMI<18.5-below normal weight; 18.5≤BMI<25-normal weight group; BMI≥25-overweight group) were matched as predictor variables, and 201 pairs were matched. Nonparametric analysis using the Mann-Whitney U test was used for diagnosing the differences between the two groups, and Kruskal-Wallis's test followed by the Mann-Whitney U test was used for analyzing the differences among three different age groups (<40, 41-60, and >60 yr group). The cut-off value of 10s G&R cycles and a combined parameter were determined using receiver operating characteristics curve analysis, area under the curve, and Youden index.

RESULTS

The authors found that little finger kinematic parameters were significantly lower in DCM patients than in asymptomatic participants. The optimal diagnostic indicator appeared to be the average of the top 10 linear accelerations with an area under the curve of 0.923.

CONCLUSION

The Finger Kinematic Test System is an objective, practical, and quantitative utility that appears to have the capacity to diagnose and evaluate the severity of DCM.

LEVEL OF EVIDENCE

3.

Artificial intelligence and ChatGPT in Orthopaedics and sports medicine

Artificial intelligence (AI) is looked upon nowadays as the potential major catalyst for the fourth industrial revolution. In the last decade, AI use in Orthopaedics increased approximately tenfold. Artificial intelligence helps with tracking activities, evaluating diagnostic images, predicting injury risk, and several other uses. Chat Generated Pre-trained Transformer (ChatGPT), which is an AI-chatbot, represents an extremely controversial topic in the academic community. The aim of this review article is to simplify the concept of AI and study the extent of AI use in Orthopaedics and sports medicine literature. Additionally, the article will also evaluate the role of ChatGPT in scientific research and publications.Level of evidence: Level V, letter to review.

Analysis of time-space variations during dynamic cervical spine motion in cervical spondylosis myelopathy patients

BACKGROUND CONTEXT

Decreased cervical range of motion (ROM) is a common symptom of myelopathy patients. Many previous studies have relied on a variety of experimental approaches for quantifying static cervical range of motion. However, the change rules of time-space variation during dynamic cervical spine motion remains unknown.

PURPOSE

To develop and validate the effectiveness of a novel wearable robot-based sensor system, Analysis of Dynamic Cervical spine Motion (ADCM), in evaluating the dynamic cervical spine motion dysfunction of patients with cervical spondylotic myelopathy (CSM).

STUDY DESIGN/SETTING

A cross-sectional study.

PATIENT SAMPLE

One hundred forty consecutive healthy individuals (70 men and 70 women) and 120 CSM patients (60 men and 60 women) were enrolled in the present study.

OUTCOME MEASURES

The cervical motion process parameters, including the flexion and extension ROM, the flexion and extension time, and the Japanese Orthopedic Association scores (JOA) for cervical spine were measured.

METHODS

Two hundred and sixty consecutive participants were asked to wear ADCM system and then fully flex and extend their neck rapidly and evenly at tolerable maximum speed. The cervical motion process was recorded and converted into waveforms. Relevant waveform parameters were measured and analyzed. The number of complete flexion-extension motions in 10 seconds has been defined as 10s F-E cycles. The Japanese Orthopedics Association (JOA) scores of CSM patients were marked.

RESULTS

CSM patients had a lower number of 10s F-E cycles than healthy subjects. There were significant differences in flexion and extension time and ROM between two groups. The waveforms of myelopathy patients were wider and lower than those in healthy individuals. The average ratio value (defined as F) of wave height to wave width (a+b/c+d) could quantitatively reflect such differences of waveforms. The average F value was correlated with the JOA scores of the cervical motion function (r=0.7538), and F value declined as JOA scores decreased. According to receiver operating characteristic curve analysis, the optimal threshold value of the normal average ratio was more than 34.7.

CONCLUSIONS

ADCM appears to be an objective and quantitative severity assessment tool for confirmed CSM patients by evaluating dynamic cervical spine motion dysfunction.

Artificial Intelligence Technologies in Neurosurgery: a Systematic Literature Review Using Topic Modeling. Part II: Research Objectives and Perspectives

The current increase in the number of publications on the use of artificial intelligence (AI) technologies in neurosurgery indicates a new trend in clinical neuroscience.

The aim of the study was to conduct a systematic literature review to highlight the main directions and trends in the use of AI in neurosurgery.

If it's information, it's not "bias": a scoping review and proposed nomenclature for future response-shift research

BACKGROUND

The growth in response-shift methods has enabled a stronger empirical foundation to investigate response-shift phenomena in quality-of-life (QOL) research; but many of these methods utilize certain language in framing the research question(s) and interpreting results that treats response-shift effects as "bias," "noise," "nuisance," or otherwise warranting removal from the results rather than as information that matters. The present project will describe the various ways in which researchers have framed the questions for investigating response-shift issues and interpreted the findings, and will develop a nomenclature for such that highlights the important information about resilience reflected by response-shift findings.

METHODS

A scoping review was done of the QOL and response-shift literature (n = 1100 articles) from 1963 to 2020. After culling only empirical response-shift articles, raters characterized how investigators framed and interpreted study research questions (n = 164 articles).

RESULTS

Of 10 methods used, papers using four of them utilized terms like "bias" and aimed to remove response-shift effects to reveal "true change." Yet, the investigators' reflections on their own conclusions suggested that they do not truly believe that response shift is error to be removed. A structured nomenclature is proposed for discussing response-shift results in a range of research contexts and response-shift detection methods.

CONCLUSIONS

It is time for a concerted and focused effort to change the nomenclature of those methods that demonstrated this misinterpretation. Only by framing and interpreting response shift as information, not bias, can we improve our understanding and methods to help to distill outcomes with and without response-shift effects.

Utility of machine learning algorithms in degenerative cervical and lumbar spine disease: a systematic review

Machine learning is a rapidly evolving field that offers physicians an innovative and comprehensive mechanism to examine various aspects of patient data. Cervical and lumbar degenerative spine disorders are commonly age-related disease processes that can utilize machine learning to improve patient outcomes with careful patient selection and intervention. The aim of this study is to examine the current applications of machine learning in cervical and lumbar degenerative spine disease. A systematic review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A search of PubMed, Embase, Medline, and Cochrane was conducted through May 31st, 2020, using the following terms: "artificial intelligence" OR "machine learning" AND "neurosurgery" AND "spine." Studies were included if original research on machine learning was utilized in patient care for degenerative spine disease, including radiographic machine learning applications. Studies focusing on robotic applications in neurosurgery, navigation, or stereotactic radiosurgery were excluded. The literature search identified 296 papers, with 35 articles meeting inclusion criteria. There were nine studies involving cervical degenerative spine disease and 26 studies on lumbar degenerative spine disease. The majority of studies for both cervical and lumbar spines utilized machine learning for the prediction of postoperative outcomes, with 5 (55.6%) and 15 (61.5%) studies, respectively. Machine learning applications focusing on degenerative lumbar spine greatly outnumber the current volume of cervical spine studies. The current research in lumbar spine also demonstrates more advanced clinical applications of radiographic, diagnostic, and predictive machine learning models.

Clinical application of Myelopathy-hand Functional Evaluation System in evaluating the postoperative hand motor function for myelopathy patients

OBJECTIVE

Recovery of hand motor function after surgical treatment in myelopathy patients is commonly observed. Accurate evaluation of postoperative hand function contributes to assessing the efficacy of surgical treatment. However, no objective and effective evaluation method has been widely accepted in clinical practice. Therefore, the study aimed to explore the value of Myelopathy-hand Functional Evaluation System (MFES) in assessing the postoperative hand function for myelopathy patients.

MATERIAL AND METHOD

MFES mainly consist of a pair of wise-gloves and a computer with software. One hundred and thirty myelopathy patients were included and all of them received optimal surgery treatment. The Japanese Orthopaedic Association (JOA) scores were marked at preoperative and at 6 months after surgery. All patients were asked to perform the 10-s grip and release test, and the hand movements were simulated and converted into waveforms by MFES. The waveform parameters were measured and analyzed.

RESULTS

The JOA scores and the number of grip-and-release (G-R) cycles significantly increased after surgery. Correspondingly, the waveforms of ulnar three fingers were significantly higher and narrower, along with the significantly declined average time per cycle in postoperative. The a/b ratio (Wave height/wave width) of five fingers were significantly higher in postoperative than that in preoperative. Based on the improvement rate of a/b, the excellent and good rate of surgical outcomes was 62.30 %, which was significantly higher than that (47.69 %) based on the improvement rate of JOA scores (P = 0.019).

CONCLUSION

MFES is an effective assessment tool in evaluating the postoperative hand function for myelopathy patients.

Clinical Application of a New Assessment Tool for Myelopathy Hand Using Virtual Reality

STUDY DESIGN

A cross-sectional study.

OBJECTIVE

To assess the effectiveness of a new assessment tool, myelopathy-hand functional evaluation system (MFES), in evaluating the hand dysfunction of patients with cervical myelopathy in the 10-second grip-and-release test (10 second G-R test).

SUMMARY OF BACKGROUND DATA

Clumsy fingers movement is a common symptom of myelopathy patients. Evaluating the impaired hand function can provide a strong basis in assessing the severity of myelopathy. Currently, no objective and effective evaluation method is widely accepted in clinical practice.

METHODS

MFES mainly consists of a pair of wise-gloves and a computer with software. One hundred and ninety-eight consecutive participants were asked to wear the wise-gloves and then perform 10 seconds G-R test. The movements of each finger were recorded by MFES and converted into waveforms. Relevant waveform parameters were measured and analyzed. The Japanese Orthopedics Association (JOA) scores of each patient were marked and the maximum spinal cord compression (MSCC) was measured on midsagittal T2-weighted magnetic resonance imaging (MRI).

RESULTS

Myelopathy patients had a lower number of G-R cycles and a longer time per cycle than healthy subjects. There were significant differences in adduction and abduction time in patients with JOA scores greater than 6, but not in healthy subjects and patients with JOA scores less than 6. The waveforms of ulnar three fingers in myelopathy patients were lower and wider than those in healthy individuals. The average ratio value of wave height to wave width (a/b) could quantitatively reflect such differences of waveforms. According to receiver operating characteristic (ROC) curve analysis, the optimal threshold value of the normal average ratio was more than 1.92. The average a/b value was correlated with the JOA scores of the motor function in the upper extremities (r = 0.842).

CONCLUSION

MFES appears to be an objective and quantitative assessment tool for patients with cervical myelopathy.

LEVEL OF EVIDENCE

3.

Changes in reaching skill in patients with cervical spondylosis after cervical decompression surgery

[Purpose] The aim of this study was to evaluate the changes in reaching function during a reaching task in cervical spondylosis (CS) patients before and after surgery. [Participants and Methods] Nine patients participated in the study. Wrist acceleration peaks were monitored pre- and postoperatively using a tri-axial accelerometer, and the Japanese Orthopedic Association (JOA) score was recorded preoperatively. Additional upper extremity function tests were performed pre- and postoperatively. Multiple stepwise regression analysis was used to investigate the contribution of wrist acceleration peak to the severity of clinical symptoms. Moreover, we compared the acceleration peaks produced during the reaching task before and after surgery. [Results] Multiple regression analysis showed that wrist acceleration peak, grip strength and pinch strength were associated with the upper extremity function of the JOA score, explaining 61.0% of the variance. There was a significant improvement in x-axis acceleration peak after surgery. [Conclusion] Our results suggested that quantitative assessments of reaching function are useful to objectively evaluate the changes in reaching function in patients undergoing cervical decompression surgery.

Machine Learning in Orthopedics: A Literature Review

In this paper we present the findings of a systematic literature review covering the articles published in the last two decades in which the authors described the application of a machine learning technique and method to an orthopedic problem or purpose. By searching both in the Scopus and Medline databases, we retrieved, screened and analyzed the content of 70 journal articles, and coded these resources following an iterative method within a Grounded Theory approach. We report the survey findings by outlining the articles' content in terms of the main machine learning techniques mentioned therein, the orthopedic application domains, the source data and the quality of their predictive performance.