Clinical validation of automated depth camera-based measurement of the Fugl-Meyer assessment for upper extremity.

OBJECTIVE: Depth camera-based measurement has demonstrated efficacy in automated assessment of upper limb Fugl-Meyer Assessment for paralysis rehabilitation. However, there is a lack of adequately sized studies to provide clinical support. Thus, we developed an automated system utilizing depth camera and machine learning, and assessed its feasibility and validity in a clinical setting. DESIGN: Validation and feasibility study of a measurement instrument based on single cross-sectional data. SETTING: Rehabilitation unit in a general hospital. PARTICIPANTS: Ninety-five patients with hemiparesis admitted for inpatient rehabilitation unit (2021-2023). MAIN MEASURES: Scores for each item, excluding those related to reflexes, were computed utilizing machine learning models trained on participant videos and readouts from force test devices, while the remaining reflex scores were derived through regression algorithms. Concurrent criterion validity was evaluated using sensitivity, specificity, percent agreement and Cohen's Kappa coefficient for ordinal scores of individual items, as well as correlations and intraclass correlation coefficients for total scores. Video-based manual assessment was also conducted and compared to the automated tools. RESULT: The majority of patients completed the assessment without therapist intervention. The automated scoring models demonstrated superior validity compared to video-based manual assessment across most items. The total scores derived from the automated assessment exhibited a high coefficient of 0.960. However, the validity of force test items utilizing force sensing resistors was relatively low. CONCLUSION: The integration of depth camera technology and machine learning models for automated Fugl-Meyer Assessment demonstrated acceptable validity and feasibility, suggesting its potential as a valuable tool in rehabilitation assessment.

Reliability of a human pose tracking algorithm for measuring upper limb joints: comparison with photography-based goniometry.

BACKGROUND: Range of motion (ROM) measurements are essential for diagnosing and evaluating upper extremity conditions. Clinical goniometry is the most commonly used methods but it is time-consuming and skill-demanding. Recent advances in human tracking algorithm suggest potential for automatic angle measuring from RGB images. It provides an attractive alternative for at-distance measuring. However, the reliability of this method has not been fully established. The purpose of this study is to evaluate if the results of algorithm are as reliable as human raters in upper limb movements. METHODS: Thirty healthy young adults (20 males, 10 females) participated in this study. Participants were asked to performed a 6-motion task including movement of shoulder, elbow and wrist. Images of movements were captured by commercial digital cameras. Each movement was measured by a pose tracking algorithm (OpenPose) and compared with the surgeon-measurement results. The mean differences between the two measurements were compared. Pearson correlation coefficients were used to determine the relationship. Reliability was investigated by the intra-class correlation coefficients. RESULTS: Comparing this algorithm-based method with manual measurement, the mean differences were less than 3 degrees in 5 motions (shoulder abduction: 0.51; shoulder elevation: 2.87; elbow flexion:0.38; elbow extension:0.65; wrist extension: 0.78) except wrist flexion. All the intra-class correlation coefficients were larger than 0.60. The Pearson coefficients also showed high correlations between the two measurements (p < 0.001). CONCLUSIONS: Our results indicated that pose estimation is a reliable method to measure the shoulder and elbow angles, supporting RGB images for measuring joint ROM. Our results presented the possibility that patients can assess their ROM by photos taken by a digital camera. TRIAL REGISTRATION: This study was registered in the Clinical Trials Center of The First Affiliated Hospital, Sun Yat-sen University (2021-387).

Validity and Reliability of a Depth Camera-Based Quantitative Measurement for Joint Motion of the Hand.

Purpose: Quantitative measurement of hand motion is essential in evaluating hand function. This study aimed to investigate the validity and reliability of a novel depth camera-based contactless automatic measurement system to assess hand range of motion and its potential benefits in clinical applications. Methods: Five hand gestures were designed to evaluate the hand range of motion using a depth camera-based measurement system. Seventy-one volunteers were enrolled in performing the designed hand gestures. Then, the hand range of motion was measured with the depth camera and manual procedures. System validity was evaluated based on 3 dimensions: repeatability, within-laboratory precision, and reproducibility. For system reliability, linear evaluation, the intraclass correlation coefficient, paired t -test and bias were employed to test the consistency and difference between the depth camera and manual procedures. Results: When measuring phalangeal length, repeatability, within-laboratory precision, and reproducibility were 2.63%, 12.87%, and 27.15%, respectively. When measuring angles of hand motion, the mean repeatability and within-laboratory precision were 1.2° and 3.3° for extension of 5 digits, 2.7° and 10.2° for flexion of 4 fingers, and 3.1° and 5.3° for abduction of 4 metacarpophalangeal joints, respectively. For system reliability, the results showed excellent consistency (intraclass correlation coefficient = 0.823; P < .05) and good linearity with the manual procedures (r = 0.909-0.982, approximately; P < .001). Besides, 78.3% of the measurements were clinically acceptable. Conclusions: Our depth camera-based evaluation system provides acceptable validity and reliability in measuring hand range of motion and offers potential benefits for clinical care and research in hand surgery. However, further studies are required before clinical application. Clinical relevance: This study suggests a depth camera-based contactless automatic measurement system holds promise for assessing hand range of motion in hand function evaluation, diagnosis, and rehabilitation for medical staff. However, it is currently not adequate for all clinical applications.

Automatic range of motion measurement via smartphone images for telemedicine examination of the hand.

BACKGROUND: Telemedicine support virtual consultations and evaluations in hand surgery for patients in remote areas during the COVID-19 era. However, traditional physical examination is challenging in telemedicine and it is inconvenient to manually measure the hand range of motion (ROM) from images or videos. Here, we propose an automatic method using the hand pose estimation technique, aiming to measure the hand ROM from smartphone images. METHODS: Twenty-eight healthy volunteers participated in the study. An eight-hand gestures measurement protocol and the Google MediaPipe Hands were used to analyze images and calculate the ROM automatically. Manual goniometry was also performed according to the guideline of the American Medical Association. The correlation between the automatic and manual methods was analyzed by the intraclass correlation coefficient and Pearson correlation coefficient. The clinical acceptance was testified using Bland-Altman plots. RESULTS: A total of 32 parameters of each hand were measured by both methods, and 1792 measurement results were compared. The mean difference between automatic and manual methods is -2.21 ± 9.29° in the angle measurement and 0.48 ± 0.48 cm in the distance measurement. The intraclass correlation coefficient of 75% of parameters was higher than 0.75, the Pearson correlation coefficient of 84% of parameters was over 0.6, and 40.6% of parameters reached well-accepted clinical agreements. CONCLUSIONS: The proposed method provides a helpful protocol for automatic hand ROM measurement based on smartphone images and the MediaPipe Hands pose estimation technique. The automatic measurement is acceptable and comparable with existing methods, showing a possible application in the telemedicine examination of hand surgery.

Precision and accuracy of measuring finger motion with a depth camera: a cross-sectional study of healthy participants.

The purpose of this cross-sectional study was to determine the precision and accuracy of the measurement of finger motion with a depth camera. Fifty-five healthy adult hands were included. Measurements were done with a depth camera and compared with traditional manual goniometer measurements. Repeated measuring showed that the overall repeatability and reproducibility of extension measured with the depth camera were within 3° and 4° and that of flexion were within 13° and 14°. Compared with traditional manual goniometry, biases of extension of all finger joints and flexion of metacarpophalangeal joints were less than 5°, and the average bias of flexion of proximal and distal interphalangeal joints was 29°. We conclude that the measurement of finger extension and flexion of the metacarpophalangeal joints with a depth camera was reliable, but improvement is required in the precision and accuracy of interphalangeal joint flexion.

Automatic Detection of Horner Syndrome by Using Facial Images.

Horner syndrome is a clinical constellation that presents with miosis, ptosis, and facial anhidrosis. It is important as a warning sign of the damaged oculosympathetic chain, potentially with serious causes. However, the diagnosis of Horner syndrome is operator dependent and subjective. This study aims to present an objective method that can recognize Horner sign from facial photos and verify its accuracy. A total of 173 images were collected, annotated, and divided into training and testing groups. Two types of classifiers were trained (two-stage classifier and one-stage classifier). The two-stage method utilized the MediaPipe face mesh to estimate the coordinates of landmarks and generate facial geometric features accordingly. Then, ten machine learning classifiers were trained based on this. The one-stage classifier was trained based on one of the latest algorithms, YOLO v5. The performance of the classifier was evaluated by the diagnosis accuracy, sensitivity, and specificity. For the two-stage model, the MediaPipe successfully detected 92.2% of images in the testing group, and the Decision Tree Classifier presented the highest accuracy (0.790). The sensitivity and specificity of this classifier were 0.432 and 0.970, respectively. As for the one-stage classifier, the accuracy, sensitivity, and specificity were 0.65, 0.51, and 0.84, respectively. The results of this study proved the possibility of automatic detection of Horner syndrome from images. This tool could work as a second advisor for neurologists by reducing subjectivity and increasing accuracy in diagnosing Horner syndrome.

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

Background: Radial, 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. Methods: Based 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. Results: The 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%. Conclusion: Our 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.

[Accuracy of key point matrix technology based contactless automatic measurement for joint motion of hand].

Objective: To validate the use of key point matrix technology based contactless automatic measurement for evaluation of joint motion of hand. Methods: Thirty-three volunteers were enrolled to evaluate the extension and flexion of hand joints between May 2021 and November 2021. There were 20 males and 13 females, the age ranged from 16 to 70 years with an average of 30.2 years. The extension angles of 14 joints of 5 fingers (including hyperextension) and the flexion angles of 12 joints of 4 fingers (excluding thumb) of volunteers were measured by key point matrix technology and manual goniometer, respectively. Then 5 participants and repeated measurement experiment were employed to test the system repeatability and accuracy; 28 participants and paired measurement experiment were employed to test the system accuracy. Results: The average repeatability of finger joint motion measured by the key point matrix technology was 1.801° (extension) and 7.823° (flexion), respectively. Compared with manual measurement, the average differences of each finger joint measured by the key point matrix technology were 3.225° in extension and 14.145° in flexion, respectively. The key point matrix technology based contactless automatic evaluation system offered excellent consistency with the manual goniometers ( ICC=0.875). While most of the consistency with manual goniometer of individual joints were at moderate levels (median of ICC, 0.440). The correlation coefficients between the measurement results of the two methods were mainly positive in the extension of the joint ( P<0.05) and negative in the flexion of the joints ( P<0.05). Conclusion: The key point matrix technology based contactless automatic evaluation provides sufficient measurement repeatability and accuracy in evaluation for the joint motion of hand.

Covering the proximal nerve stump with chondroitin sulfate proteoglycans prevents traumatic painful neuroma formation by blocking axon regeneration after neurotomy in Sprague Dawley rats.

OBJECTIVE: Neuropathic pain caused by traumatic neuromas is an extremely intractable clinical problem. Disorderly scar tissue accumulation and irregular and immature axon regeneration around the injury site mainly contribute to traumatic painful neuroma formation. Therefore, successfully preventing traumatic painful neuroma formation requires the effective inhibition of irregular axon regeneration and disorderly accumulation of scar tissue. Considering that chondroitin sulfate proteoglycans (CSPGs) can act on the growth cone and effectively inhibit axon regeneration, the authors designed and manufactured a CSPG-gelatin blocker to regulate the CSPGs' spatial distribution artificially and applied it in a rat model after sciatic nerve neurectomy to evaluate its effects in preventing traumatic painful neuroma formation. METHODS: Sixty female Sprague Dawley rats were randomly divided into three groups (positive group: no covering; blank group: covering with gelatin blocker; and CSPG group: covering with the CSPG-gelatin blocker). Pain-related factors were evaluated 2 and 8 weeks postoperatively (n = 30). Neuroma growth, autotomy behavior, and histological features of the neuromas were assessed 8 weeks postoperatively (n = 30). RESULTS: Eight weeks postoperatively, typical bulb-shaped neuromas did not form in the CSPG group, and autotomy behavior was obviously better in the CSPG group (p < 0.01) than in the other two groups. Also, in the CSPG group the regenerated axons showed a lower density and more regular and improved myelination (p < 0.01). Additionally, the distribution and density of collagenous fibers and the expression of α-smooth muscle actin were significantly lower in the CSPG group than in the positive group (p < 0.01). Regarding pain-related factors, c-fos, substance P, interleukin (IL)-17, and IL-1ß levels were significantly lower in the CSPG group than those in the positive and blank groups 2 weeks postoperatively (p < 0.05), while substance P and IL-17 remained lower in the CSPG group 8 weeks postoperatively (p < 0.05). CONCLUSIONS: The authors found that CSPGs loaded in a gelatin blocker can prevent traumatic neuroma formation and effectively relieve pain symptoms after sciatic nerve neurotomy by blocking irregular axon regeneration and disorderly collagenous fiber accumulation in the proximal nerve stump. These results indicate that covering the proximal nerve stump with CSPGs may be a new and promising strategy to prevent traumatic painful neuroma formation in the clinical setting.

Customized Scaffold Design Based on Natural Peripheral Nerve Fascicle Characteristics for Biofabrication in Tissue Regeneration.

OBJECTIVE: The use of a biofabrication nerve scaffold, which mimics the nerve microstructure, as an alternative for autologous nerve transplantation is a promising strategy for treating peripheral nerve defects. This study aimed to design a customized biofabrication scaffold model with the characteristics of human peripheral nerve fascicles. METHODS: We used Micro-MRI technique to obtain different nerve fascicles. A full-length 28 cm tibial nerve specimen was obtained and was divided into 14 two-centimetre nerve segments. 3D models of the nerve fascicles were obtained by three-dimensional reconstruction after image segmentation. The central line of the nerve fascicles was fitted, and the aggregation of nerve fascicles was analysed quantitatively. The nerve scaffold was designed by simulating the clinical nerve defect and extracting information from the acquired nerve fascicle data; the scaffold design was displayed by 3D printing to verify the accuracy of the model. RESULT: The microstructure of the sciatic nerve, tibial nerve, and common peroneal nerve in the nerve fascicles could be obtained by three-dimensional reconstruction. The number of cross fusions of tibial nerve fascicles from proximal end to distal end decreased gradually. By designing the nerve graft in accordance with the microstructure of the nerve fascicles, the 3D printed model demonstrated that the two ends of the nerve defect can be well matched. CONCLUSION: The microstructure of the nerve fascicles is complicated and changeable, and the spatial position of each nerve fascicle and the long segment of the nerve fascicle aggregation show great changes at different levels. Under the premise of the stability of the existing imaging techniques, a large number of scanning nerve samples can be used to set up a three-dimensional database of the peripheral nerve fascicle microstructure, integrating the gross imaging information, and provide a template for the design of the downstream nerve graft model.

Salting-out homogenous extraction followed by ionic liquid/ionic liquid liquid-liquid micro-extraction for determination of sulfonamides in blood by high performance liquid chromatography.

Salting-out homogenous extraction followed by ionic liquid/ionic liquid dispersive liquid-liquid micro-extraction system was developed and applied to the extraction of sulfonamides in blood. High-performance liquid chromatography was applied to the determination of the analytes. The blood sample was centrifuged to obtain the serum. After the proteins in the serum were removed in the presence of acetonitrile, ionic liquid 1-butyl-3-methylimidazolium tetrafluoroborate, dipotassium hydrogen phosphate, ionic liquid 1-Hexyl-3-methylimidazolium hexafluorophosphate were added into the resulting solution. After the resulting mixture was ultrasonically shaken and centrifuged, the precipitate was separated. The acetonitrile was added in the precipitate and the analytes were extracted into the acetonitrile phase. The parameters affecting the extraction efficiency, such as volume of ionic liquid, amount of dipotassium hydrogen phosphate, volume of dispersant, extraction time and temperature were investigated. The limits of detection of sulfamethizole (STZ), sulfachlorpyridazine (SCP), sulfamethoxazole (SMX) and Sulfisoxazole (SSZ) were 4.78, 3.99, 5.21 and 3.77µgL-1, respectively. When the present method was applied to the analysis of real blood samples, the recoveries of analytes ranged from 90.0% to 113.0% and relative standard deviations were lower than 7.2%.