Outcomes of 2,154 Endoscopic Trigger Finger Release Procedures.

BACKGROUND: Trigger finger is one of the most frequent causes of hand pain and disability. Recently, an endoscopic trigger finger release technique was developed, but outcomes have rarely been reported. Here, we present the outcomes of 2,154 endoscopic trigger finger release procedures in a single center. METHODS: In this retrospective study, 2,154 endoscopic trigger finger release procedures were performed on 2,034 patients. Outcome assessment at 90 days after surgery was classified as excellent, good, fair or poor according to a combination of patient satisfaction with the scar and pain at rest or under load on a numeric rating scale. RESULTS: The therapeutic outcomes were: 1,027 excellent, 607 good, 400 fair, and none poor. No major surgical complications were observed. Minor complications occurred in 231 fingers (10.7%). CONCLUSIONS: All patients were satisfied with their outcome after endoscopic trigger finger release. Endoscopic release can be an effective and efficient therapeutic method for the treatment of trigger finger.

Convolutional neural network algorithm trained on lumbar spine radiographs to predict outcomes of transforaminal epidural steroid injection for lumbosacral radicular pain from spinal stenosis.

Little is known about the therapeutic outcomes of transforaminal epidural steroid injection (TFESI) in patients with lumbosacral radicular pain due to lumbar spinal stenosis (LSS). Using lumbar spine radiographs as input data, we trained a convolutional neural network (CNN) to predict therapeutic outcomes after lumbar TFESI in patients with lumbosacral radicular pain caused by LSS. We retrospectively recruited 193 patients for this study. The lumbar spine radiographs included anteroposterior, lateral, and bilateral (left and right) oblique views. We cut each lumbar spine radiograph image into a square shape that included the vertebra corresponding to the level at which the TFESI was performed and the vertebrae juxta below and above that level. Output data were divided into "favorable outcome" (≥ 50% reduction in the numeric rating scale [NRS] score at 2 months post-TFESI) and "poor outcome" (< 50% reduction in the NRS score at 2 months post-TFESI). Using these input and output data, we developed a CNN model for predicting TFESI outcomes. The area under the curve of our model was 0.920. Its accuracy was 87.2%. Our CNN model has an excellent capacity for predicting therapeutic outcomes after lumbar TFESI in patients with lumbosacral radicular pain induced by LSS.

Protocol for lower back pain management: Insights from the French healthcare system.

In this editorial we comment on the article published in a recent issue of the World Journal of Clinical Cases. This article described a novel ultrasound-guided lateral recess block approach in treating a patient with lateral recess stenosis. The impact of spinal pain-related disability extends significantly, causing substantial human suffering and medical costs. Each county has its preferred treatment strategies for spinal pain. Here, we explore the lower back pain (LBP) treatment algorithm recommended in France. The treatment algorithm for LBP recommended by the French National Authority for Health emphasizes early patient activity and minimal medication use. It encourages the continuation of daily activities, limits excessive medication and spinal injections, and incorporates psychological assessments and non-pharmacological therapies for chronic cases. However, the algorithm may not aggressively address acute pain in the early stages, potentially delaying relief and increasing the risk of chronicity. Additionally, the recommended infiltrations primarily involve caudal epidural steroid injections, with limited consideration for other injection procedures, such as transforaminal or interlaminar epidural steroid injections. The fixed follow-up timeline may not accommodate patients who do not respond to initial treatment or experience intense pain, potentially delaying the exploration of alternative therapies. Despite these limitations, understanding the strengths and weaknesses of the French approach could inform adaptations in LBP treatment strategies globally, potentially enhancing patient outcomes and satisfaction across diverse healthcare systems.

Lacertus notch as a sign of lacertus syndrome.

OBJECTIVE: Many clinicians are unfamiliar with a diagnosis of lacertus syndrome (LS). We investigated the value of the lacertus notch sign in diagnosing LS. METHODS: We included 56 consecutive patients (112 upper extremities) who had neuropathic pain and neurological symptoms of the hand. The presence of LS and the lacertus notch sign in each upper extremity was assessed. RESULTS: Of the 83 upper extremities with LS, 54 (65.1%) had a lacertus notch sign, whereas 29 (34.9%) did not. Of the 29 upper extremities without LS, 9 (31.0%) and 20 (69.0%) had and did not have a lacertus notch sign, respectively. The rates of lacertus notch presence in upper extremities with and without LS were significantly different. Of the 63 upper extremities with a lacertus notch sign, 54 (85.7%) were diagnosed with LS, whereas 9 (14.3%) were not. Of the 49 upper extremities without a lacertus notch sign, 20 (40.8%) were diagnosed with LS, and 29 (59.2%) were not. We observed significant differences in the rates of LS in upper extremities with and without lacertus notch. CONCLUSIONS: The presence of the lacertus notch sign is useful for diagnosing LS. When patients with neuropathic pain and neurological symptoms present with a lacertus notch sign, clinicians should consider the possibility of LS.

Description of WALANT technique in open bone and ligament wrist surgery.

The gold-standard for bone, ligament and joint surgery in the wrist is locoregional anesthesia in most countries. Wide-Awake Local Anesthesia No Tourniquet (WALANT) is commonly used for simple soft-tissue hand surgery procedures such as carpal tunnel or trigger finger release, and can now also be safely used in procedures such as proximal row carpectomy, scapholunate ligament repair or partial wrist fusion, to name but a few. This article describes the use of WALANT for complex surgery in the wrist. WALANT surgery offers many known benefits, such as enhanced patient safety and comfort, simplified perioperative process and avoidance of anesthesia-related risks, and also allows the surgeon to perform intraoperative testing of the repaired structures. Thus, the surgeon can tailor the rehabilitation program and shorten recovery time. We describe detailed guidelines for performing WALANT procedures safely and effectively, making it a favorable option for complex surgeries in the wrist.

Deep learning algorithm for automatically measuring Cobb angle in patients with idiopathic scoliosis.

PURPOSE: The Cobb angle is a standard measurement to qualify and track the progression of scoliosis. However, the Cobb angle has high inter- and intra-observer variability. Consequently, its measurement varies with vertebrae and may even differ when the same vertebra is measured. Therefore, it is not constant and differs with measurements. This study aimed to develop a deep learning model that automatically measures the Cobb angle. The deep learning model for identifying vertebrae on spine radiographs was developed. METHODS: The dataset consisted of 297 images that were divided into two subsets for training and validation. Two hundred and twenty-seven images (76.4%) were used to train the model, while 70 images (23.6%) were used as the validation dataset. Absolut error between the measurements by the observer and developed deep learning model and intraclass correlation coefficient (ICC). RESULTS: The average absolute error between the measurements was 1.97° with a standard deviation of 1.57°. In addition, 95.9% of the angles had an absolute error of less than 5°. The ICC was calculated to assess the model's reliability further. The ICC was 0.981, indicating excellent reliability. CONCLUSIONS: The authors believe the model will be useful in clinical practice by relieving clinicians of the burden of having to manually compute the Cobb angle. Further studies are needed to enhance the accuracy and versatility of this deep learning model.

The Short-Term Outcome of Transforaminal Epidural Steroid Injection in Patients with Radicular Pain Due to Foraminal Stenosis from Lumbar Isthmic Spondylolisthesis.

Purpose: In this study, we evaluated the therapeutic outcomes of transforaminal epidural steroid injection (TFESI) in managing chronic radicular pain due to foraminal stenosis. Furthermore, we evaluated its effectiveness according to isthmic spondylolisthesis (IS) severity. Patients and Methods: We included 40 patients with radicular pain due to IS-derived foraminal stenosis in our study and treated them with TFESI. Two patients were lost during follow-up. Based on the lateral lumbar radiograph findings, we allocated the recruited patients with < 25% slippage by IS to Group 1 (n = 23) and those having 25-50% slippage to Group 2 (n = 15). The degree of pain was measured using a numeric rating scale (NRS) at pre-treatment and 1 and 2 months after TFESI. Results: In 38 patients who completed the study, the NRS at pre-treatment was significantly reduced at the 1- and 2-month follow-ups. In the Group analysis, the NRS scores were significantly reduced after TFESI in both Groups 1 and 2, regardless of IS severity. However, the reduction in NRS scores 1 month after TFESI was significantly greater in Group 1 than in Group 2. Moreover, the rate of successful treatment outcomes was significantly higher (65.2%) in Group 1 than in Group 2 (26.7%). Conclusion: After TFESI, chronic radicular pain was significantly reduced regardless of IS severity, and its effect persisted for at least 2 months. However, its effect was superior when the vertebra slippage by IS was less than 25% compared to patients with 25%-50%.

Use of artificial intelligence in the field of pain medicine.

In this editorial we comment on the article "Potential and limitations of ChatGPT and generative artificial intelligence in medial safety education" published in the recent issue of the World Journal of Clinical Cases. This article described the usefulness of artificial intelligence (AI) in medial safety education. Herein, we focus specifically on the use of AI in the field of pain medicine. AI technology has emerged as a powerful tool, and is expected to play an important role in the healthcare sector and significantly contribute to pain medicine as further developments are made. AI may have several applications in pain medicine. First, AI can assist in selecting testing methods to identify causes of pain and improve diagnostic accuracy. Entry of a patient's symptoms into the algorithm can prompt it to suggest necessary tests and possible diagnoses. Based on the latest medical information and recent research results, AI can support doctors in making accurate diagnoses and setting up an effective treatment plan. Second, AI assists in interpreting medical images. For neural and musculoskeletal disorders, imaging tests are of vital importance. AI can analyze a variety of imaging data, including that from radiography, computed tomography, and magnetic resonance imaging, to identify specific patterns, allowing quick and accurate image interpretation. Third, AI can predict the outcomes of pain treatments, contributing to setting up the optimal treatment plan. By predicting individual patient responses to treatment, AI algorithms can assist doctors in establishing a treatment plan tailored to each patient, further enhancing treatment effectiveness. For efficient utilization of AI in the pain medicine field, it is crucial to enhance the accuracy of AI decision-making by using more medical data, while issues related to the protection of patient personal information and responsibility for AI decisions will have to be addressed. In the future, AI technology is expected to be innovatively applied in the field of pain medicine. The advancement of AI is anticipated to have a positive impact on the entire medical field by providing patients with accurate and effective medical services.

Finite Element Analysis of Stress Distribution and Range of Motion in Discogenic Back Pain.

Objective: Precise knowledge regarding the mechanical stress applied to the intervertebral disc following each individual spine motion enables physicians and patients to understand how people with discogenic back pain should be guided in their exercises and which spine motions to specifically avoid. We created an intervertebral disc degeneration model and conducted a finite element (FE) analysis of loaded stresses following each spinal posture or motion. Methods: A three-dimensional FE model of intervertebral disc degeneration at L4-5 was constructed. The intervertebral disc degeneration model was created according to the modified Dallas discogram scale. The Von Mises stress and range of motion (ROM) regarding the intervertebral discs and the endplates were analyzed. Results: We observed that mechanical stresses loaded onto the intervertebral discs were similar during flexion, extension, and lateral bending, which were greater than those occurring during torsion. Based on the comparison among the grades divided by the modified Dallas discogram scale, the mechanical stress during extension was greater in grades 3-5 than it was during the others. During extension, the mechanical stress loaded onto the intervertebral disc and endplate was greatest in the posterior portion. Mechanical stresses loaded onto the intervertebral disc were greater in grades 3-5 compared to those in grades 0-2. Conclusion: Our findings suggest that it might be beneficial for patients experiencing discogenic back pain to maintain a neutral posture in their lumbar spine when engaging in daily activities and exercises, especially those suffering from significant intravertebral disc degeneration.

Association of Range of Motion Deficit and Recurrence of Pain After Treatment of Adhesive Capsulitis.

INTRODUCTION: We evaluated the factors influencing the duration of significant pain reduction after conservative management for adhesive capsulitis (AC). METHODS: Follow-up for 6-8 months was performed with 141 patients with AC who experienced significant pain reduction after treatment. Clinical and demographic factors, numeric rating scale (NRS) scores, and shoulder range of motion (ROM) were collected and assessed pretreatment (T0), at 5 weeks post-treatment (T1), and at 6-8 months post-treatment (T2). Patients were divided into successful (n = 96) and unsuccessful (n = 45) NRS groups according to the degree of pain reduction at T2. We assessed post-treatment NRS and ROM improvement scores within each group and compared these parameters between the two groups. RESULTS: Significant NRS and ROM improvements were achieved in all patients who participated in our study. The unsuccessful NRS group demonstrated a lack of significant improvement in abduction at T1 and T2. All T1 and shoulder ROM measurements among the unsuccessful NRS group were significantly smaller than those among the successful NRS group. CONCLUSIONS: Failure to achieve a significant improvement in abduction angle after conservative management of AC was significantly associated with pain recurrence.

Identification of L5 vertebra on lumbar spine radiographs using deep learning.

OBJECTIVE: Deep learning is an advanced machine-learning approach that is used in several medical fields. Here, we developed a deep learning model using an object detection algorithm to identify the L5 vertebra on anteroposterior lumbar spine radiographs, and assessed its detection accuracy. METHODS: We retrospectively recruited 150 participants for whom both anteroposterior whole-spine and lumbar spine radiographs were available. The anteroposterior lumbar spine radiographs of these patients were used as the input data. Of the 150 images, 105 (70%) were randomly selected as the training set, and the remaining 45 (30%) were assigned to the validation set. YOLOv5x, of the YOLOv5 family model, was used to detect the L5 vertebra area. RESULTS: The mean average precisions 0.5 and 0.75 of the trained L5 detection model were 99.2% and 96.9%, respectively. The model's precision was 95.7% and its recall was 97.8%. Furthermore, 93.3% of the validation data were correctly detected. CONCLUSION: Our deep learning model showed an outstanding ability to identify L5 vertebrae.

Chronic venous insufficiency, could it be one of the missing pieces in the puzzle of treating pain?

Pain is a common complaint among patients seeking medical care. If left untreated, pain can become chronic, significantly affecting patients' quality of life. An accurate diagnosis of the underlying cause of pain is crucial for effective treatment. Chronic venous insufficiency (CVI) is frequently overlooked by pain physicians. Moreover, many pain physicians lack sufficient knowledge about CVI. CVI is a common condition resulting from malfunctioning or damaged valves in lower limb veins. Symptoms of CVI, ranging from mild to severe, include pain, heaviness, fatigue, itching, swelling, skin color changes, and ulcers in the lower limbs. Recently, it has become more widely known that these symptoms can be attributed to CVI. Even slight or mild CVI can cause related symptoms. Pain physicians primarily consider neuromusculoskeletal disorders when assessing patients with leg pain, and often neglect the possibility of CVI. In clinical practice, when pain physicians encounter patients with unresolved leg pain, they must assess whether the patients exhibit symptoms of CVI and conduct tests to differentiate CVI from other potential causes.

Deep Learning Algorithm Trained on Oblique Cervical Radiographs to Predict Outcomes of Transforaminal Epidural Steroid Injection for Pain from Cervical Foraminal Stenosis.

INTRODUCTION: We developed a convolutional neural network (CNN) model to predict treatment outcomes of transforaminal epidural steroid injection (TFESI) for controlling cervical radicular pain due to cervical foraminal stenosis. METHODS: We retrospectively recruited 293 patients with cervical TFESI due to cervical radicular pain caused by cervical foraminal stenosis. We obtained a single oblique cervical radiograph from each patient. We cut each oblique cervical radiograph image into a square shape, including the foramen that was targeted for TFESI, the intervertebral disc, the facet joint of the corresponding level with the targeted foramen, and the pedicles of the vertebral bodies just above and below the targeted foramen. Therefore, images including the targeted foramen and structures around the targeted foramen were used as input data. A favorable outcome was defined as a ≥ 50% reduction in the numeric rating scale (NRS) score at 2 months post TFESI compared to the pretreatment NRS score. A poor outcome was defined as a < 50% reduction in the NRS score at 2 months post TFESI vs. the pretreatment score. RESULTS: The area under the curve of our developed model for predicting the treatment outcome of cervical TFESI in patients with cervical foraminal stenosis was 0.823. CONCLUSION: A CNN model trained using oblique cervical radiographs can be helpful in predicting treatment outcomes after cervical TFESI in patients with cervical foraminal stenosis. If the predictive accuracy is increased, we believe that the deep learning model using cervical radiographs as input data can be easily and widely used in clinics or hospitals.

Diagnostic and prognostic significance of preoperative evoked potential tests in degenerative cervical myelopathy.

BACKGROUND CONTEXT: Decompression surgery is a treatment option for patients with degenerative cervical myelopathy (DCM). Surgical decisions primarily depend on clinical symptoms and radiological examinations. The diagnostic and prognostic significance of evoked potential tests for surgical outcomes in patients with DCM has not been thoroughly examined. PURPOSE: To identify the diagnostic and prognostic significance of preoperative evoked potential tests in patients with DCM who underwent decompression surgery. STUDY DESIGN: This was a retrospective observational study. PATIENT SAMPLE: One hundred two consecutive patients who underwent evoked potential tests and surgical treatment between January 2016 and December 2020 in a single spine center and had a minimum follow-up of 6 months. OUTCOME MEASURES: Japanese Orthopedic Association (JOA) scores obtained preoperatively and 6 months after surgery. METHODS: This study evaluated the preoperative central motor conduction time (CMCT), somatosensory evoked potentials, and Japanese Orthopedic Association (JOA) scores obtained preoperatively and 6 months after surgery. RESULTS: Abnormal CMCT findings were observed in 94 patients (92.2%). Abnormal somatosensory evoked potentials were observed in 77 patients (75.5%). There was a statistically significant correlation between preoperative JOA score and abductor pollicis brevis (APB)-CMCT (r=-0.546, p=.001), tibialis anterior (TA)-CMCT (r=-0.517, p<.001), median nerve (MN)-SSEP (r=-0.353, p=.001), and tibial nerve (TN)-SSEP (r=-0.349, p=.003). There were significant differences in recovery rates associated with diabetes mellitus (DM), preoperative severity of myelopathy, TA-CMCT, MN-SSEP, and TN-SSEP. Stepwise multiple regression analysis showed that the major factors affecting the clinical outcomes were TN-SSEP (ß=0.327, p=.004), preoperative JOA score (ß=0.278, p=.012), and DM (ß=0.241, p=.025). CONCLUSIONS: Evoked potential testing is a functional diagnostic tool that can indicate the severity of myelopathic symptoms in patients with DCM. Additionally, preoperative TN-SSEP may have significant prognostic value in predicting postoperative clinical outcomes. Thus, preoperative evoked potential tests could be helpful for determining suitable surgical treatment candidates and forecasting postoperative prognosis.

Flexor Tenosynovial Fistula as a Complication after Endoscopic Trigger Finger Release: A Case Report.

OBJECTIVE: We report a rare case of flexor tenosynovial fistula secondary to endoscopic release of the A1 pulley for treatment of trigger finger. CASE PRESENTATION: A 72-year-old woman underwent endoscopic release of the A1 pulleys of her left ring and right middle fingers. Nine days after surgery, the wound at the base of the proximal phalanx of the ring finger (distal portal) remained open and a clear liquid discharge was seen. The volume of discharge increased with active finger motion. However, there was no evidence of infection. The patient was diagnosed with tenosynovial fistula as a complication of endoscopic release of the A1 pulley. At day 30, the fistula and drainage persisted and the condition was managed by surgical excision of the fistula and primary closure. The wound then healed completely. CONCLUSION: Our report alerts hand surgeons to the potential development of flexor tenosynovial fistula as a very rare complication following endoscopic release of the A1 pulley for the treatment of trigger finger.