Development of a convolutional neural network for the identification and the measurement of the median nerve on ultrasound images acquired at carpal tunnel level

Carpal tunnel syndrome prediction with machine learning algorithms using anthropometric and strength-based measurement

OBJECTIVES

Carpal tunnel syndrome (CTS) stands as the most prevalent upper extremity entrapment neuropathy, with a multifaceted etiology encompassing various risk factors. This study aimed to investigate whether anthropometric measurements of the hand, grip strength, and pinch strength could serve as predictive indicators for CTS through machine learning techniques.

METHODS

Enrollment encompassed patients exhibiting CTS symptoms (n = 56) and asymptomatic healthy controls (n = 56), with confirmation via electrophysiological assessments. Anthropometric measurements of the hand were obtained using a digital caliper, grip strength was gauged via a digital handgrip dynamometer, and pinch strengths were assessed using a pinchmeter. A comprehensive analysis was conducted employing four most common and effective machine learning algorithms, integrating thorough parameter tuning and cross-validation procedures. Additionally, the outcomes of variable importance were presented.

RESULTS

Among the diverse algorithms, Random Forests (accuracy of 89.474%, F1-score of 0.905, and kappa value of 0.789) and XGBoost (accuracy of 86.842%, F1-score of 0.878, and kappa value of 0.736) emerged as the top-performing choices based on distinct classification metrics. In addition, using variable importance calculations specific to these models, the most important variables were found to be wrist circumference, hand width, hand grip strength, tip pinch, key pinch, and middle finger length.

CONCLUSION

The findings of this study demonstrated that wrist circumference, hand width, hand grip strength, tip pinch, key pinch, and middle finger length can be utilized as reliable indicators of CTS. Also, the model developed herein, along with the identified crucial variables, could serve as an informative guide for healthcare professionals, enhancing precision and efficacy in CTS prediction.

Deep learning in rheumatological image interpretation

Artificial intelligence techniques, specifically deep learning, have already affected daily life in a wide range of areas. Likewise, initial applications have been explored in rheumatology. Deep learning might not easily surpass the accuracy of classic techniques when performing classification or regression on low-dimensional numerical data. With images as input, however, deep learning has become so successful that it has already outperformed the majority of conventional image-processing techniques developed during the past 50 years. As with any new imaging technology, rheumatologists and radiologists need to consider adapting their arsenal of diagnostic, prognostic and monitoring tools, and even their clinical role and collaborations. This adaptation requires a basic understanding of the technical background of deep learning, to efficiently utilize its benefits but also to recognize its drawbacks and pitfalls, as blindly relying on deep learning might be at odds with its capabilities. To facilitate such an understanding, it is necessary to provide an overview of deep-learning techniques for automatic image analysis in detecting, quantifying, predicting and monitoring rheumatic diseases, and of currently published deep-learning applications in radiological imaging for rheumatology, with critical assessment of possible limitations, errors and confounders, and conceivable consequences for rheumatologists and radiologists in clinical practice.

The role of artificial intelligence in electrodiagnostic and neuromuscular medicine: Current state and future directions

The rapid advancements in artificial intelligence (AI), including machine learning (ML), and deep learning (DL) have ushered in a new era of technological breakthroughs in healthcare. These technologies are revolutionizing the way we utilize medical data, enabling improved disease classification, more precise diagnoses, better treatment selection, therapeutic monitoring, and highly accurate prognostication. Different ML and DL models have been used to distinguish between electromyography signals in normal individuals and those with amyotrophic lateral sclerosis and myopathy, with accuracy ranging from 67% to 99.5%. DL models have also been successfully applied in neuromuscular ultrasound, with the use of segmentation techniques achieving diagnostic accuracy of at least 90% for nerve entrapment disorders, and 87% for inflammatory myopathies. Other successful AI applications include prediction of treatment response, and prognostication including prediction of intensive care unit admissions for patients with myasthenia gravis. Despite these remarkable strides, significant knowledge, attitude, and practice gaps persist, including within the field of electrodiagnostic and neuromuscular medicine. In this narrative review, we highlight the fundamental principles of AI and draw parallels with the intricacies of human brain networks. Specifically, we explore the immense potential that AI holds for applications in electrodiagnostic studies, neuromuscular ultrasound, and other aspects of neuromuscular medicine. While there are exciting possibilities for the future, it is essential to acknowledge and understand the limitations of AI and take proactive steps to mitigate these challenges. This collective endeavor holds immense potential for the advancement of healthcare through the strategic and responsible integration of AI technologies.

One-Stop Automated Diagnostic System for Carpal Tunnel Syndrome in Ultrasound Images Using Deep Learning

OBJECTIVE

Ultrasound (US) examination has unique advantages in diagnosing carpal tunnel syndrome (CTS), although identification of the median nerve (MN) and diagnosis of CTS depend heavily on the expertise of examiners. In the aim of alleviating this problem, we developed a one-stop automated CTS diagnosis system (OSA-CTSD) and evaluated its effectiveness as a computer-aided diagnostic tool.

METHODS

We combined real-time MN delineation, accurate biometric measurements and explainable CTS diagnosis into a unified framework, called OSA-CTSD. We then collected a total of 32,301 static images from US videos of 90 normal wrists and 40 CTS wrists for evaluation using a simplified scanning protocol.

RESULTS

The proposed model exhibited better segmentation and measurement performance than competing methods, with a Hausdorff distance (95th percentile) score of 7.21 px, average symmetric surface distance score of 2.64 px, Dice score of 85.78% and intersection over union score of 76.00%. In the reader study, it exhibited performance comparable to the average performance of experienced radiologists in classifying CTS and outperformed inexperienced radiologists in terms of classification metrics (e.g., accuracy score 3.59% higher and F1 score 5.85% higher).

CONCLUSION

Diagnostic performance of the OSA-CTSD was promising, with the advantages of real-time delineation, automation and clinical interpretability. The application of such a tool not only reduces reliance on the expertise of examiners but also can help to promote future standardization of the CTS diagnostic process, benefiting both patients and radiologists.

Computer vision detects inflammatory arthritis in standardized smartphone photographs in an Indian patient cohort

Introduction

Computer vision extracts meaning from pixelated images and holds promise in automating various clinical tasks. Convolutional neural networks (CNNs), a deep learning network used therein, have shown promise in analyzing X-ray images and joint photographs. We studied the performance of a CNN on standardized smartphone photographs in detecting inflammation in three hand joints and compared it to a rheumatologist's diagnosis.

Methods

We enrolled 100 consecutive patients with inflammatory arthritis with an onset period of less than 2 years, excluding those with deformities. Each patient was examined by a rheumatologist, and the presence of synovitis in each joint was recorded. Hand photographs were taken in a standardized manner, anonymized, and cropped to include joints of interest. A ResNet-101 backbone modified for two class outputs (inflamed or not) was used for training. We also tested a hue-augmented dataset. We reported accuracy, sensitivity, and specificity for three joints: wrist, index finger proximal interphalangeal (IFPIP), and middle finger proximal interphalangeal (MFPIP), taking the rheumatologist's opinion as the gold standard.

Results

The cohort consisted of 100 individuals, of which 22 of them were men, with a mean age of 49.7 (SD 12.9) years. The majority of the cohort (n = 68, 68%) had rheumatoid arthritis. The wrist (125/200, 62.5%), MFPIP (94/200, 47%), and IFPIP (83/200, 41.5%) were the three most commonly inflamed joints. The CNN achieved the highest accuracy, sensitivity, and specificity in detecting synovitis in the MFPIP (83, 77, and 88%, respectively), followed by the IFPIP (74, 74, and 75%, respectively) and the wrist (62, 90, and 21%, respectively).

Discussion

We have demonstrated that computer vision was able to detect inflammation in three joints of the hand with reasonable accuracy on standardized photographs despite a small dataset. Feature engineering was not required, and the CNN worked despite a diversity in clinical diagnosis. Larger datasets are likely to improve accuracy and help explain the basis of classification. These data suggest a potential use of computer vision in screening and follow-up of inflammatory arthritis.

Deep Learning-Based Approaches for Enhanced Diagnosis and Comprehensive Understanding of Carpal Tunnel Syndrome

Carpal tunnel syndrome (CTS) is a prevalent medical condition resulting from compression of the median nerve in the hand, often caused by overuse or age-related factors. In this study, a total of 160 patients participated, including 80 individuals with CTS presenting varying levels of severity across different age groups. Numerous studies have explored the use of machine learning (ML) and deep learning (DL) techniques for CTS diagnosis. However, further research is required to fully leverage the potential of artificial intelligence (AI) technology in CTS diagnosis, addressing the challenges and limitations highlighted in the existing literature. In our work, we propose a novel approach for CTS diagnosis, prediction, and monitoring disease progression. The proposed framework consists of three main layers. Firstly, we employ three distinct DL models for CTS diagnosis. Through our experiments, the proposed approach demonstrates superior performance across multiple evaluation metrics, with an accuracy of 0.969%, precision of 0.982%, and recall of 0.963%. The second layer focuses on predicting the cross-sectional area (CSA) at 1, 3, and 6 months using ML models, aiming to forecast disease progression during therapy. The best-performing model achieves an accuracy of 0.9522, an R2 score of 0.667, a mean absolute error (MAE) of 0.0132, and a median squared error (MdSE) of 0.0639. The highest predictive performance is observed after 6 months. The third layer concentrates on assessing significant changes in the patients' health status through statistical tests, including significance tests, the Kruskal-Wallis test, and a two-way ANOVA test. These tests aim to determine the effect of injections on CTS treatment. The results reveal a highly significant reduction in symptoms, as evidenced by scores from the Symptom Severity Scale and Functional Status Scale, as well as a decrease in CSA after 1, 3, and 6 months following the injection. SHAP is then utilized to provide an understandable explanation of the final prediction. Overall, our study presents a comprehensive approach for CTS diagnosis, prediction, and monitoring, showcasing promising results in terms of accuracy, precision, and recall for CTS diagnosis, as well as effective prediction of disease progression and evaluation of treatment effectiveness through statistical analysis.

Deep Radiomics Features of Median Nerves for Automated Diagnosis of Carpal Tunnel Syndrome With Ultrasound Images: A Multi-Center Study

OBJECTIVES

Ultrasound is widely used in diagnosing carpal tunnel syndrome (CTS). However, the limitations of ultrasound in CTS detection are the lack of objective measures in the detection of nerve abnormality and the operator-dependent nature of ultrasound imaging. Therefore, in this study, we developed and proposed externally validated artificial intelligence (AI) models based on deep-radiomics features.

METHODS

We have used 416 median nerves from 2 countries (Iran and Colombia) for the development (112 entrapped and 112 normal nerves from Iran) and validation (26 entrapped and 26 normal nerves from Iran, and 70 entrapped and 70 normal nerves from Columbia) of our models. Ultrasound images were fed to the SqueezNet architecture to extract deep-radiomics features. Then a ReliefF method was used to select the clinically significant features. The selected deep-radiomics features were fed to 9 common machine-learning algorithms to choose the best-performing classifier. The 2 best-performing AI models were then externally validated.

RESULTS

Our developed model achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.910 (88.46% sensitivity, 88.46% specificity) and 0.908 (84.62% sensitivity, 88.46% specificity) with support vector machine and stochastic gradient descent (SGD), respectively using the internal validation dataset. Furthermore, both models consistently performed well in the external validation dataset, and achieved an AUC of 0.890 (85.71% sensitivity, 82.86% specificity) and 0.890 (84.29% sensitivity and 82.86% specificity), with SVM and SGD models, respectively.

CONCLUSION

Our proposed AI models fed with deep-radiomics features performed consistently with internal and external datasets. This justifies that our proposed system can be employed for clinical use in hospitals and polyclinics.

Real-Time Automated Segmentation of Median Nerve in Dynamic Ultrasonography Using Deep Learning

OBJECTIVE

The morphological dynamics of the median nerve across the level extracted from dynamic ultrasonography are valuable for the diagnosis and evaluation of carpal tunnel syndrome (CTS), but the data extraction requires tremendous labor to manually segment the nerve across the image sequence. Our aim was to provide visually real-time, automated median nerve segmentation and subsequent data extraction in dynamic ultrasonography.

METHODS

We proposed a deep-learning model modified from SOLOv2 and tailored for median nerve segmentation. Ensemble strategies combining several state-of-the-art models were also employed to examine whether the segmentation accuracy could be improved. Image data were acquired from nine normal participants and 59 patients with idiopathic CTS.

DISCUSSION

Our model outperformed several state-of-the-art models with respect to inference speed, whereas the segmentation accuracy was on a par with that achieved by these models. When evaluated on a single 1080Ti GPU card, our model achieved an intersection over union score of 0.855 and Dice coefficient of 0.922 at 28.9 frames/s. The ensemble models slightly improved segmentation accuracy.

CONCLUSION

Our model has great potential for use in the clinical setting, as the real-time, automated extraction of the morphological dynamics of the median nerve allows clinicians to diagnose and treat CTS as the images are acquired.

Application of deep learning algorithms in automatic sonographic localization and segmentation of the median nerve: A systematic review and meta-analysis

OBJECTIVE

High-resolution ultrasound is an emerging tool for diagnosing carpal tunnel syndrome caused by the compression of the median nerve at the wrist. This systematic review and meta-analysis aimed to explore and summarize the performance of deep learning algorithms in the automatic sonographic assessment of the median nerve at the carpal tunnel level.

METHODS

PubMed, Medline, Embase, and Web of Science were searched from the earliest records to May 2022 for studies investigating the utility of deep neural networks in the evaluation of the median nerve in carpal tunnel syndrome. The quality of the included studies was evaluated using the Quality Assessment Tool for Diagnostic Accuracy Studies. The outcome variables included precision, recall, accuracy, F-score, and Dice coefficient.

RESULTS

In total, seven articles were included, comprising 373 participants. The deep learning and related algorithms comprised U-Net, phase-based probabilistic active contour, MaskTrack, ConvLSTM, DeepNerve, DeepSL, ResNet, Feature Pyramid Network, DeepLab, Mask R-CNN, region proposal network, and ROI Align. The pooled values of precision and recall were 0.917 (95 % confidence interval [CI], 0.873-0.961) and 0.940 (95 % CI, 0.892-0.988), respectively. The pooled accuracy and Dice coefficient were 0.924 (95 % CI, 0.840-1.008) and 0.898 (95 % CI, 0.872-0.923), respectively, whereas the summarized F-score was 0.904 (95 % CI, 0.871-0.937).

CONCLUSION

The deep learning algorithm enables automated localization and segmentation of the median nerve at the carpal tunnel level in ultrasound imaging with acceptable accuracy and precision. Future research is expected to validate the performance of deep learning algorithms in detecting and segmenting the median nerve along its entire length as well as across datasets obtained from various ultrasound manufacturers.

Degree of Accuracy With Which Deep Learning for Ultrasound Images Identifies Osteochondritis Dissecans of the Humeral Capitellum

BACKGROUND

Medical screening using ultrasonography (US) has been performed on young baseball players for early detection of osteochondritis dissecans (OCD) of the humeral capitellum. Deep learning (DL) and artificial intelligence (AI) techniques are widely adopted in the medical imaging research field.

PURPOSE/HYPOTHESIS

The purpose of this study was to calculate the diagnostic accuracy using DL for US images of OCD. We hypothesized that using DL for US imaging would improve the prediction accuracy of OCD.

STUDY DESIGN

Cohort study (Diagnosis); Level of evidence, 2.

METHODS

A total of 40 elbows (mean age of patients, 12.1 years) that were suspected of having OCD at a medical checkup and later confirmed by radiographs and magnetic resonance imaging were included in the study. The affected elbows were used as the OCD group and the contralateral elbows as the control group. From US videos, 100 images per elbow were captured from different angles, and 4000 images of the elbows were prepared for both groups. Of these, 80% were randomly selected by DL models and used as training data; the remaining were used as test data. Transfer learning was conducted using 3 pretrained DL models. The confusion matrix and the area under the receiver operating characteristic curve (AUC) were used to evaluate the model, and the visualization of the areas deemed important by the DL models was also performed. Furthermore, OCD regions were detected using an automatic image recognition model based on DL.

RESULTS

Classification of the OCD image by the DL model was performed; the best accuracy score was 0.87; the recall was 1.00. AUC was high for all DL models. Visualization of important features showed that AI predicted the presence of OCD by focusing on the irregularity or discontinuity of the surface of subchondral bone. In the detection of OCD task, the mean average precision was 0.83.

CONCLUSION

The DL on US images identified OCD with high accuracy. The important features detected by the DL models correspond to the areas used by clinicians in screening the US images. The OCD was also detected with high accuracy using the object detection model. The AI model may be used in medical screening for OCD.

Predicting CTS Diagnosis and Prognosis Based on Machine Learning Techniques

Carpal tunnel syndrome (CTS) is a clinical disease that occurs due to compression of the median nerve in the carpal tunnel. The determination of the severity of carpal tunnel syndrome is essential to provide appropriate therapeutic interventions. Machine learning (ML)-based modeling can be used to classify diseases, make decisions, and create new therapeutic interventions. It is also used in medical research to implement predictive models. However, despite the growth in medical research based on ML and Deep Learning (DL), CTS research is still relatively scarce. While a few studies have developed models to predict diagnosis of CTS, no ML model has been presented to classify the severity of CTS based on comprehensive clinical data. Therefore, this study developed new classification models for determining CTS severity using ML algorithms. This study included 80 patients with other diseases that have an overlap in symptoms with CTS, such as cervical radiculopathysasas, de quervian tendinopathy, and peripheral neuropathy, and 80 CTS patients who underwent ultrasonography (US)-guided median nerve hydrodissection. CTS severity was classified into mild, moderate, and severe grades. In our study, we aggregated the data from CTS patients and patients with other diseases that have an overlap in symptoms with CTS, such as cervical radiculopathysasas, de quervian tendinopathy, and peripheral neuropathy. The dataset was randomly split into training and test data, at 70% and 30%, respectively. The proposed model achieved promising results of 0.955%, 0.963%, and 0.919% in terms of classification accuracy, precision, and recall, respectively. In addition, we developed a machine learning model that predicts the probability of a patient improving after the hydro-dissection injection process based on the aggregated data after three different months (one, three, and six). The proposed model achieved accuracy after six months of 0.912%, after three months of 0.901%, and after one month 0.877%. The overall performance for predicting the prognosis after six months outperforms the prediction after one and three months. We utilized statistics tests (significance test, Spearman's correlation test, and two-way ANOVA test) to determine the effect of injection process in CTS treatment. Our data-driven decision support tools can be used to help determine which patients to operate on in order to avoid the associated risks and expenses of surgery.

Using deep learning for ultrasound images to diagnose carpal tunnel syndrome with high accuracy

Recently, deep learning (DL) algorithms have been adapted for the diagnosis of medical images. The purpose of this study was to detect image features using DL without measuring median nerve cross-sectional area (CSA) in ultrasonography (US) images of carpal tunnel syndrome (CTS) and calculate the diagnostic accuracy from the confusion matrix obtained. US images of 50 hands without CTS and 50 hands diagnosed with CTS were used in this study. The short-axis image of the median nerve was visualized, and 5000 images of both groups were prepared. Forty hands in each group were used as training data for the DL algorithm, while the remainder were used as test data. Transfer learning was performed using three pre-trained models. The confusion matrix and receiver operating characteristic curves were used to evaluate diagnostic accuracy. Furthermore, regions where DL was determined to be important were visualized. The highest score had an accuracy of 0.96, precision of 0.99 and recall of 0.94. Visualization of the important features revealed that the DL models focused on the epineurium of the median nerve and the surrounding soft tissue. The proposed technique enables the accurate prediction of CTS without measurement of the CSA.

A deep learning approach to median nerve evaluation in ultrasound images of carpal tunnel inlet

Ultrasound (US) imaging is recognized as a useful support for Carpal Tunnel Syndrome (CTS) assessment through the evaluation of median nerve morphology. However, US is still far to be systematically adopted to evaluate this common entrapment neuropathy, due to US intrinsic challenges, such as its operator dependency and the lack of standard protocols. To support sonographers, the present study proposes a fully-automatic deep learning approach to median nerve segmentation from US images. We collected and annotated a dataset of 246 images acquired in clinical practice involving 103 rheumatic patients, regardless of anatomical variants (bifid nerve, closed vessels). We developed a Mask R-CNN with two additional transposed layers at segmentation head to accurately segment the median nerve directly on transverse US images. We calculated the cross-sectional area (CSA) of the predicted median nerve. Proposed model achieved good performances both in median nerve detection and segmentation: Precision (Prec), Recall (Rec), Mean Average Precision (mAP) and Dice Similarity Coefficient (DSC) values are 0.916 ± 0.245, 0.938 ± 0.233, 0.936 ± 0.235 and 0.868 ± 0.201, respectively. The CSA values measured on true positive predictions were comparable with the sonographer manual measurements with a mean absolute error (MAE) of 0.918 mm2. Experimental results showed the potential of proposed model, which identified and segmented the median nerve section in normal anatomy images, while still struggling when dealing with infrequent anatomical variants. Future research will expand the dataset including a wider spectrum of normal anatomy and pathology to support sonographers in daily practice.

Graphical abstract

RATING: Medical knowledge-guided rheumatoid arthritis assessment from multimodal ultrasound images via deep learning

Multimodal ultrasound has demonstrated its power in the clinical assessment of rheumatoid arthritis (RA). However, for radiologists, it requires strong experience. In this paper, we propose a rheumatoid arthritis knowledge guided (RATING) system that automatically scores the RA activity and generates interpretable features to assist radiologists' decision-making based on deep learning. RATING leverages the complementary advantages of multimodal ultrasound images and solves the limited training data problem with self-supervised pretraining. RATING outperforms all of the existing methods, achieving an accuracy of 86.1% on a prospective test dataset and 85.0% on an external test dataset. A reader study demonstrates that the RATING system improves the average accuracy of 10 radiologists from 41.4% to 64.0%. As an assistive tool, not only can RATING indicate the possible lesions and enhance the diagnostic performance with multimodal ultrasound but it can also enlighten the road to human-machine collaboration in healthcare.

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RATING is a medical knowledge-guided deep learning system for RA assessment

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It leverages diagnostic paradigm and experience to enhance the robustness

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Self-supervised pretraining ensures reliability even with limited training data

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A clinical reader study demonstrates its effectiveness in assisting RA assessment

Rheumatoid arthritis (RA) has detrimental outcomes, including increased disability and mortality. To enhance the clinical assessment of RA, we propose a rheumatoid arthritis knowledge guided (RATING) system for scoring RA activity from multimodal ultrasound images. It combines the knowledge of clinical diagnosis with deep learning, serving as an example of designing deep learning systems for handling real clinical problems. We further integrated the system into the clinical decision-making process via human-machine collaboration and demonstrated significant improvements in assessment performance. We expect that our research will illuminate the road to human-machine collaboration and help transform clinical diagnostics and precision medicine in a wider range of biomedical research.