Diagnosis of Cubital Tunnel Syndrome Using Deep Learning on Ultrasonographic Images

Cubital tunnel syndrome: anatomy, pathology, and imaging

Cubital tunnel syndrome (CuTS) is the second most common peripheral neuropathy in the upper limb. It occurs due to ulnar nerve compression within the fibro-osseous cubital tunnel at the elbow joint. Although CuTS is typically diagnosed clinically and with electrodiagnostic studies, the importance of imaging in evaluating the condition is growing. Knowing the typical imaging findings of ulnar nerve entrapment is necessary for precise diagnosis and proper treatment. In this article, we focus on the clinical features, workup and complex imaging of the "anatomic" cubital tunnel and relevant pathological entities.

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.

Role of high-resolution ultrasound and magnetic resonance neurography in the evaluation of peripheral nerves in the upper extremity

Upper extremity entrapment neuropathies are common conditions in which peripheral nerves are prone to injury at specific anatomical locations, particularly superficial regions or within fibro-osseous tunnels, resulting in pain and potential disability. Although neuropathy is primarily diagnosed clinically by physical examination and electrophysiology, imaging evaluation with ultrasound and magnetic resonance neurography are valuable complementary non-invasive and accurate tools for evaluation and can help define the site and cause of nerve dysfunction which ultimately leads to precise and timely treatment. Ultrasound, which has higher spatial resolution, can quickly and comfortably characterize the peripheral nerves in real time and can evaluate for denervation related muscle atrophy. Magnetic resonance imaging on the other hand provides excellent contrast resolution between the nerves and adjacent tissues, also between pathologic and normal segments of peripheral nerves. It can also assess the degree of muscle denervation and atrophy. As a prerequisite for nerve imaging, radiologists and sonographers should have a thorough knowledge of anatomy of the peripheral nerves and their superficial and deep branches, including variant anatomy, and the motor and sensory territories innervated by each nerve. The purpose of this illustrative article is to review the common neuropathy and nerve entrapment syndromes in the upper extremities focusing on ultrasound and magnetic resonance neurography imaging.

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.

Ulnar nerve thickness at the elbow on longitudinal ultrasound view in control subjects

INTRODUCTION

Ulnar mononeuropathy at the elbow is the second most frequent neuropathy in humans. Diagnosis is based on clinical and electrophysiological criteria and, more recently, also on ultrasound. Cross-sectional ultrasound is currently the most valued, although longitudinal ultrasound allows assessment of the entire affected trajectory of the nerve in a single view, but always in a straight line with no changes in direction, as in the extended elbow. The main aim of this work is to propose normative values ​​for longitudinal ultrasound of the ulnar nerve at the elbow.

METHODS

The neurological exploration of upper extremity, and electrophysiological and ultrasound parameters at the elbow of ulnar nerve were evaluated in 76 limbs from 38 asymptomatic subjects.

RESULTS

The diameters of the nerve as well as the distal and proximal areas were larger at the proximal region of the ulnar groove, and even more so in older individuals. In most of these elderly subjects, we found a small, non-significant slowdown in motor conduction velocity at the elbow with respect to the forearm (less than 5 m/s).

CONCLUSIONS

We observed a good correlation between the longitudinal and cross-sectional ultrasounds of the ulnar nerve at the elbow. Longitudinal ultrasound proved to be sensitive, reliable, simple and rapid, but its greatest contribution was allowing the visualization of the entire nerve trajectory in an integrated way, providing an image with good definition of the outline, proportions and intraneural characteristics of the nerve.