Identification of unapproved orthopedic regenerative medicine: Usefulness of the Act on Safety of Regenerative Medicine.

In Japan, the Act on Safety of Regenerative Medicine regulates unapproved regenerative medicine. Other nations market regenerative medicine products, bypassing regulatory approval. To identify unapproved orthopedic regenerative medicine, we have used data based on the Act. Platelet-rich plasma was often used. The common target was the knee. Prices averaged $2,490.

A Comparison Between GPT-3.5, GPT-4, and GPT-4V: Can the Large Language Model (ChatGPT) Pass the Japanese Board of Orthopaedic Surgery Examination?

Introduction Recently, large-scale language models, such as ChatGPT (OpenAI, San Francisco, CA), have evolved. These models are designed to think and act like humans and possess a broad range of specialized knowledge. GPT-3.5 was reported to be at a level of passing the United States Medical Licensing Examination. Its capabilities continue to evolve, and in October 2023, GPT-4V became available as a model capable of image recognition. Therefore, it is important to know the current performance of these models because they will be soon incorporated into medical practice. We aimed to evaluate the performance of ChatGPT in the field of orthopedic surgery. Methods We used three years' worth of Japanese Board of Orthopaedic Surgery Examinations (JBOSE) conducted in 2021, 2022, and 2023. Questions and their multiple-choice answers were used in their original Japanese form, as was the official examination rubric. We inputted these questions into three versions of ChatGPT: GPT-3.5, GPT-4, and GPT-4V. For image-based questions, we inputted only textual statements for GPT-3.5 and GPT-4, and both image and textual statements for GPT-4V. As the minimum scoring rate acquired to pass is not officially disclosed, it was calculated using publicly available data. Results The estimated minimum scoring rate acquired to pass was calculated as 50.1% (43.7-53.8%). For GPT-4, even when answering all questions, including the image-based ones, the percentage of correct answers was 59% (55-61%) and GPT-4 was able to achieve the passing line. When excluding image-based questions, the score reached 67% (63-73%). For GPT-3.5, the percentage was limited to 30% (28-32%), and this version could not pass the examination. There was a significant difference in the performance between GPT-4 and GPT-3.5 (p < 0.001). For image-based questions, the percentage of correct answers was 25% in GPT-3.5, 38% in GPT-4, and 38% in GPT-4V. There was no significant difference in the performance for image-based questions between GPT-4 and GPT-4V. Conclusions ChatGPT had enough performance to pass the orthopedic specialist examination. After adding further training data such as images, ChatGPT is expected to be applied to the orthopedics field.

Automated entry of paper-based patient-reported outcomes: Applying deep learning to the Japanese orthopaedic association back pain evaluation questionnaire.

BACKGROUND: Health-related patient-reported outcomes (HR-PROs) are crucial for assessing the quality of life among individuals experiencing low back pain. However, manual data entry from paper forms, while convenient for patients, imposes a considerable tallying burden on collectors. In this study, we developed a deep learning (DL) model capable of automatically reading these paper forms. METHODS: We employed the Japanese Orthopaedic Association Back Pain Evaluation Questionnaire, a globally recognized assessment tool for low back pain. The questionnaire comprised 25 low back pain-related multiple-choice questions and three pain-related visual analog scales (VASs). We collected 1305 forms from an academic medical center as the training set, and 483 forms from a community medical center as the test set. The performance of our DL model for multiple-choice questions was evaluated using accuracy as a categorical classification task. The performance for VASs was evaluated using the correlation coefficient and absolute error as regression tasks. RESULT: In external validation, the mean accuracy of the categorical questions was 0.997. When outputs for categorical questions with low probability (threshold: 0.9996) were excluded, the accuracy reached 1.000 for the remaining 65 % of questions. Regarding the VASs, the average of the correlation coefficients was 0.989, with the mean absolute error being 0.25. CONCLUSION: Our DL model demonstrated remarkable accuracy and correlation coefficients when automatic reading paper-based HR-PROs during external validation.

Development of patient-reported outcome for adult spinal deformity: validation study.

Adult spinal deformity (ASD) is a complex condition that combines scoliosis, kyphosis, pain, and postoperative range of motion limitation. The lack of a scale that can successfully capture this complex condition is a clinical challenge. We aimed to develop a disease-specific scale for ASD. The study included 106 patients (mean age; 68 years, 89 women) with ASD. We selected 29 questions that could be useful in assessing ASD and asked the patients to answer them. The factor analysis found two factors: the main symptom and the collateral symptom. The main symptom consisted of 10 questions and assessed activity of daily living (ADL), pain, and appearance. The collateral symptom consisted of five questions to assess ADL due to range of motion limitation. Cronbach's alpha was 0.90 and 0.84, respectively. The Spearman's correlation coefficient between the change of main symptom and satisfaction was 0.48 (p < 0.001). The effect size of Cohen's d for comparison between preoperative and postoperative scores was 1.09 in the main symptom and 0.65 in the collateral symptom. In conclusion, we have developed a validated disease-specific scale for ASD that can simultaneously evaluate the benefits and limitations of ASD surgery with enough responsiveness in clinical practice.

Risk Factor Analysis of Surgery-related Complications in Primary Thoracic Spine Surgery for Degenerative Diseases and Characteristics of the Patients Also Undergoing Surgery on the Cervical and/or Lumbar Spine.

STUDY DESIGN: A retrospective cohort study using prospectively collected data. OBJECTIVE: This study primarily aimed to investigate the risk factors for surgery-related complications in primary thoracic spine surgery for degenerative diseases using a surgeon-maintained database. The secondary purpose was to elucidate the characteristics of surgically treated thoracic myelopathy that also required cervical and/or lumbar spine surgery in the study period. SUMMARY OF BACKGROUND DATA: Few studies reported surgical complications and the feature of tandem spinal stenosis in thoracic myelopathy in detail because of their rarity. MATERIALS AND METHODS: This study included 840 thoracic myelopathy patients undergoing primary surgery for degenerative diseases from 2012 to 2021, investigating the effects of diseases, surgical procedures, and patient demographics on postoperative neurological deterioration, dural tear, dural leakage, surgical-site infection, and postoperative hematoma. In thoracic myelopathy patients who were surgically treated and also undergoing cervical and/or lumbar surgery, we investigated the proportion, the effects of diseases, and the order and intervals between surgeries. RESULTS: Multivariate logistic regression revealed that significant risk factors ( P <0.05) for postoperative neurological deterioration were intervertebral disk herniation [odds ratio (OR): 4.59, 95% confidence interval (CI): 1.32-16.0) and degenerative spondylolisthesis (OR: 11.1, 95% CI: 2.15-57.5). Ossification of the ligamentum flavum (OR: 4.12, 95% CI: 1.92-8.86), anterior spinal fusion (OR: 41.2, 95% CI: 4.70-361), and circumferential decompression via a posterior approach (OR: 30.5, 95% CI: 2.27-410) were risk factors for dural tear. In thoracic myelopathy patients surgically treated, 37.0% also underwent degenerative cervical and/or lumbar surgery. CONCLUSIONS: Pathologies involving anterior decompression and instability increased the risk of postoperative neurological deterioration. The risk of dural tear was increased when dura mater adhesions were likely to be directly operated upon. It should be recognized that a relatively high proportion (37.0%) of surgically treated thoracic myelopathy patients also underwent cervical and/or lumbar surgery.

Bimodal artificial intelligence using TabNet for differentiating spinal cord tumors-Integration of patient background information and images.

We proposed a bimodal artificial intelligence that integrates patient information with images to diagnose spinal cord tumors. Our model combines TabNet, a state-of-the-art deep learning model for tabular data for patient information, and a convolutional neural network for images. As training data, we collected 259 spinal tumor patients (158 for schwannoma and 101 for meningioma). We compared the performance of the image-only unimodal model, table-only unimodal model, bimodal model using a gradient-boosting decision tree, and bimodal model using TabNet. Our proposed bimodal model using TabNet performed best (area under the receiver-operating characteristic curve [AUROC]: 0.91) in the training data and significantly outperformed the physicians' performance. In the external validation using 62 cases from the other two facilities, our bimodal model showed an AUROC of 0.92, proving the robustness of the model. The bimodal analysis using TabNet was effective for differentiating spinal tumors.

Review of Basic Research about Ossification of the Spinal Ligaments Focusing on Animal Models.

Ossification of the posterior longitudinal ligament (OPLL) is a heterotopic ossification that may cause spinal cord compression. With the recent development of computed tomography (CT) imaging, it is known that patients with OPLL often have complications related to ossification of other spinal ligaments, and OPLL is now considered part of ossification of the spinal ligaments (OSL). OSL is known to be a multifactorial disease with associated genetic and environmental factors, but its pathophysiology has not been clearly elucidated. To elucidate the pathophysiology of OSL and develop novel therapeutic strategies, clinically relevant and validated animal models are needed. In this review, we focus on animal models that have been reported to date and discuss their pathophysiology and clinical relevance. The purpose of this review is to summarize the usefulness and problems of existing animal models and to help further the development of basic research on OSL.

Development of artificial intelligence for automated measurement of cervical lordosis on lateral radiographs.

Cervical sagittal alignment is an essential parameter for the evaluation of spine disorders. Manual measurement is time-consuming and burdensome to measurers. Artificial intelligence (AI) in the form of convolutional neural networks has begun to be used to measure x-rays. This study aimed to develop AI for automated measurement of lordosis on lateral cervical x-rays. We included 4546 cervical x-rays from 1674 patients. For all x-rays, the caudal endplates of C2 and C7 were labeled based on consensus among well-experienced spine surgeons, the data for which were used as ground truth. This ground truth was split into training data and test data, and the AI model learned the training data. The absolute error of the AI measurements relative to the ground truth for 4546 x-rays was determined by fivefold cross-validation. Additionally, the absolute error of AI measurements was compared with the error of other 2 surgeons' measurements on 415 radiographs of 168 randomly selected patients. In fivefold cross-validation, the absolute error of the AI model was 3.3° in the average and 2.2° in the median. For comparison of other surgeons, the mean absolute error for measurement of 168 patients was 3.1° ± 3.4° for the AI model, 3.9° ± 3.4° for Surgeon 1, and 3.8° ± 4.7° for Surgeon 2. The AI model had a significantly smaller error than Surgeon 1 and Surgeon 2 (P = 0.002 and 0.036). This algorithm is available at ( https://ykszk.github.io/c2c7demo/ ). The AI model measured cervical spine alignment with better accuracy than surgeons. AI can assist in routine medical care and can be helpful in research that measures large numbers of images. However, because of the large errors in rare cases such as highly deformed ones, AI may, in principle, be limited to assisting humans.

Prostaglandin EP4 Selective Agonist AKDS001 Enhances New Bone Formation by Minimodeling in a Rat Heterotopic Xenograft Model of Human Bone.

To enhance bone regeneration, the use of bone morphogenetic protein (BMP)-2 is an attractive option. Unfortunately, the dose-dependent side effects prevent its widespread use. Therefore, a novel osteogenic agent using a different mechanism of action than BMP-2 is highly desirable. Previous reports demonstrated that prostaglandin E2 receptor 4 (EP4) agonists have potent osteogenic effects on non-human cells and are one of the potential alternatives for BMP-2. Here, we investigated the effects of an EP4 agonist (AKDS001) on human cells with a rat heterotopic xenograft model of human bone. Bone formation in the xenograft model was significantly enhanced by AKDS001 treatment. Histomorphometric analysis showed that the mode of bone formation by AKDS001 was minimodeling rather than remodeling. In cultured human mesenchymal stem cells, AKDS001 enhanced osteogenic differentiation and mineralization via the cAMP/PKA pathway. In cultured human preosteoclasts, AKDS001 suppressed bone resorption by inhibiting differentiation into mature osteoclasts. Thus, we conclude that AKDS001 can enhance bone formation in grafted autogenous bone by minimodeling while maintaining the volume of grafted bone. The combined use of an EP4 agonist and autogenous bone grafting may be a novel treatment option to enhance bone regeneration. However, we should be careful in interpreting the results because male xenografts were implanted in male rats in the present study. It remains to be seen whether females can benefit from the positive effects of AKDS001 MS by using female xenografts implanted in female rats in clinically relevant animal models.