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

Carpal tunnel syndrome

Carpal tunnel syndrome (CTS) is the most common nerve entrapment disorder worldwide. The epidemiology and risk factors, including family burden, for developing CTS are multi-factorial. Despite much research, its intricate pathophysiological mechanism(s) are not fully understood. An underlying subclinical neuropathy may indicate an increased susceptibility to developing CTS. Although surgery is often performed for CTS, clear international guidelines to indicate when to perform non-surgical or surgical treatment, based on stage and severity of CTS, remain to be elucidated. Neurophysiological examination, using electrophysiology or ultrasonography, performed in certain circumstances, should correlate with the history and findings in clinical examination of the person with CTS. History and clinical examination are particularly relevant globally owing to lack of other equipment. Various instruments are used to assess CTS and treatment outcomes as well as the effect of the disorder on quality of life. The surgical treatment options of CTS - open or endoscopic - offer an effective solution to mitigate functional impairments and pain. However, there are risks of post-operative persistent or recurrent symptoms, requiring meticulous diagnostic re-evaluation before any additional surgery. Health-care professionals should have increased awareness about CTS and all its implications. Future considerations of CTS include use of linked national registries to understand risk factors, explore possible screening methods, and evaluate diagnosis and treatment with a broader perspective beyond surgery, including psychological well-being.

Deep Learning Detection and Segmentation of Facet Joints in Ultrasound Images Based on Convolutional Neural Networks and Enhanced Data Annotation

The facet joint injection is the most common procedure used to release lower back pain. In this paper, we proposed a deep learning method for detecting and segmenting facet joints in ultrasound images based on convolutional neural networks (CNNs) and enhanced data annotation. In the enhanced data annotation, a facet joint was considered as the first target and the ventral complex as the second target to improve the capability of CNNs in recognizing the facet joint. A total of 300 cases of patients undergoing pain treatment were included. The ultrasound images were captured and labeled by two professional anesthesiologists, and then augmented to train a deep learning model based on the Mask Region-based CNN (Mask R-CNN). The performance of the deep learning model was evaluated using the average precision (AP) on the testing sets. The data augmentation and data annotation methods were found to improve the AP. The AP50 for facet joint detection and segmentation was 90.4% and 85.0%, respectively, demonstrating the satisfying performance of the deep learning model. We presented a deep learning method for facet joint detection and segmentation in ultrasound images based on enhanced data annotation and the Mask R-CNN. The feasibility and potential of deep learning techniques in facet joint ultrasound image analysis have been demonstrated.

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.

Correlation between Harris hip score and gait analysis through artificial intelligence pose estimation in patients after total hip arthroplasty

BACKGROUND

Recently, open pose estimation using artificial intelligence (AI) has enabled the analysis of time series of human movements through digital video inputs. Analyzing a person's actual movement as a digitized image would give objectivity in evaluating a person's physical function. In the present study, we investigated the relationship of AI camera-based open pose estimation with Harris Hip Score (HHS) developed for patient-reported outcome (PRO) of hip joint function.

METHOD

HHS evaluation and pose estimation using AI camera were performed for a total of 56 patients after total hip arthroplasty in Gyeongsang National University Hospital. Joint angles and gait parameters were analyzed by extracting joint points from time-series data of the patient's movements. A total of 65 parameters were from raw data of the lower extremity. Principal component analysis (PCA) was used to find main parameters. K-means cluster, X-squared test, Random forest, and mean decrease Gini (MDG) graph were also applied.

RESULTS

The train model showed 75% prediction accuracy and the test model showed 81.8% reality prediction accuracy in Random forest. "Anklerang_max", "kneeankle_diff", and "anklerang_rl" showed the top 3 Gini importance score in the Mean Decrease Gini (MDG) graph.

CONCLUSION

The present study shows that pose estimation data using AI camera is related to HHS by presenting associated gait parameters. In addition, our results suggest that ankle angle associated parameters could be key factors of gait analysis in patients who undergo total hip arthroplasty.

A comprehensive review on federated learning based models for healthcare applications

A disease is an abnormal condition that negatively impacts the functioning of the human body. Pathology determines the causes behind the disease and identifies its development mechanism and functional consequences. Each disease has different identification methods, including X-ray scans for pneumonia, covid-19, and lung cancer, whereas biopsy and CT-scan can identify the presence of skin cancer and Alzheimer's disease, respectively. Early disease detection leads to effective treatment and avoids abiding complications. Deep learning has provided a vast number of applications in medical sectors resulting in accurate and reliable early disease predictions. These models are utilized in the healthcare industry to provide supplementary assistance to doctors in identifying the presence of diseases. Majorly, these models are trained through secondary data sources since healthcare institutions refrain from sharing patients' private data to ensure confidentiality, which limits the effectiveness of deep learning models due to the requirement of extensive datasets for training to achieve optimal results. Federated learning deals with the data in such a way that it doesn't exploit the privacy of a patient's data. In this work, a wide variety of disease detection models trained through federated learning have been rigorously reviewed. This meta-analysis provides an in-depth review of the federated learning architectures, federated learning types, hyperparameters, dataset utilization details, aggregation techniques, performance measures, and augmentation methods applied in the existing models during the development phase. The review also highlights various open challenges associated with the disease detection models trained through federated learning for future research.

The Potential of Ultrasound Radiomics in Carpal Tunnel Syndrome Diagnosis: A Systematic Review and Meta-Analysis

Background: Carpal tunnel syndrome (CTS) is the most common entrapment neuropathy for which ultrasound imaging has recently emerged as a valuable diagnostic tool. This meta-analysis aims to investigate the role of ultrasound radiomics in the diagnosis of CTS and compare it with other diagnostic approaches. Methods: We conducted a comprehensive search of electronic databases from inception to September 2023. The included studies were assessed for quality using the Quality Assessment Tool for Diagnostic Accuracy Studies. The primary outcome was the diagnostic performance of ultrasound radiomics compared to radiologist evaluation for diagnosing CTS. Results: Our meta-analysis included five observational studies comprising 840 participants. In the context of radiologist evaluation, the combined statistics for sensitivity, specificity, and diagnostic odds ratio were 0.78 (95% confidence interval (CI), 0.71 to 0.83), 0.72 (95% CI, 0.59 to 0.81), and 9 (95% CI, 5 to 15), respectively. In contrast, the ultrasound radiomics training mode yielded a combined sensitivity of 0.88 (95% CI, 0.85 to 0.91), a specificity of 0.88 (95% CI, 0.84 to 0.92), and a diagnostic odds ratio of 58 (95% CI, 38 to 87). Similarly, the ultrasound radiomics testing mode demonstrated an aggregated sensitivity of 0.85 (95% CI, 0.78 to 0.89), a specificity of 0.80 (95% CI, 0.73 to 0.85), and a diagnostic odds ratio of 22 (95% CI, 12 to 41). Conclusions: In contrast to assessments by radiologists, ultrasound radiomics exhibited superior diagnostic performance in detecting CTS. Furthermore, there was minimal variability in the diagnostic accuracy between the training and testing sets of ultrasound radiomics, highlighting its potential as a robust diagnostic tool in CTS.

Body mass index and carpal tunnel syndrome: A case-control study

To investigate the electrophysiological characteristics of carpal tunnel syndrome (CTS) and to evaluate the relationship between electrophysiological indexes and body mass index (BMI). In the analysis of 153 hospitalized patients with CTS, the median motor conduction velocity, motor conduction amplitude, motor conduction latency, sensor conduction velocity, sensor conduction amplitude and median sensory latency were analyzed. BMI was calculated. Total 171 healthy individuals were selected as control group. According to Guidelines for Prevention and Control of Overweight and Obesity in Chinese Adults, patients were divided into groups A, B and C. Patients with BMI (kg/m2) <24 were classified into group A; those with 24 ≤ BMI < 28 were regarded as overweight and classified into group B; and those with BMI ≥ 28 were regarded as obese and classified into group C. The BMI of CTS patients was significantly higher than that of healthy individuals (P < .05). For the sensory nerve, with the increase of BMI, the incubation period was gradually prolonged and the conduction velocity gradually decreases (P < .05). In terms of motor latency, with an increase in BMI, the latency showed a trend of first decreasing and then increasing, while the conduction velocity showed a trend of first increasing and then decreasing (P < .05). Electrophysiological examination plays an important supporting role in the diagnosis of CTS. BMI is positively correlated with the degree of CTS injury to a certain extent. Weight loss can effectively prevent the occurrence of CTS and slow the progression of nerve damage in CTS patients.

Deep learning algorithm for predicting subacromial motion trajectory: Dynamic shoulder ultrasound analysis

Subacromial motion metrics can be extracted from dynamic shoulder ultrasonography, which is useful for identifying abnormal motion patterns in painful shoulders. However, frame-by-frame manual labeling of anatomical landmarks in ultrasound images is time consuming. The present study aims to investigate the feasibility of a deep learning algorithm for extracting subacromial motion metrics from dynamic ultrasonography. Dynamic ultrasound imaging was retrieved by asking 17 participants to perform cyclic shoulder abduction and adduction along the scapular plane, whereby the trajectory of the humeral greater tubercle (in relation to the lateral acromion) was depicted by the deep learning algorithm. Extraction of the subacromial motion metrics was conducted using a convolutional neural network (CNN) or a self-transfer learning-based (STL)-CNN with or without an autoencoder (AE). The mean absolute error (MAE) compared with the manually-labeled data (ground truth) served as the main outcome variable. Using eight-fold cross-validation, the average MAE was proven to be significantly higher in the group using CNN than in those using STL-CNN or STL-CNN+AE for the relative difference between the greater tubercle and lateral acromion on the horizontal axis. The MAE for the localization of the two aforementioned landmarks on the vertical axis also seemed to be enlarged in those using CNN compared with those using STL-CNN. In the testing dataset, the errors in relation to the ground truth for the minimal vertical acromiohumeral distance were 0.081-0.333 cm using CNN, compared with 0.002-0.007 cm using STL-CNN. We successfully demonstrated the feasibility of a deep learning algorithm for automatic detection of the greater tubercle and lateral acromion during dynamic shoulder ultrasonography. Our framework also demonstrated the capability of capturing the minimal vertical acromiohumeral distance, which is the most important indicator of subacromial motion metrics in daily clinical practice.