No abstract available.
Spine*

Tentative diagnosis of meniscal pathology is very helpful for the successful operative management including arthroscopy. Conventional arthrography has long been used to meet this purpose, but it has some limitations due to complex views and some difficulties in imaging the whole anatomical structures. Recent diagnostic advancement produced post-arthrographic HRCT and MRI for the compensation of these problems of conventional arthrography. Arthrography and post-arthrographic HRCT has been carried out on 49 knees in 43 patients. 38 knees among them could be confirmed by arthroscopy and following results were obtained. 1. Diagnostic accuracies of post-arthrographic HRCT for medial and lateral meniscal injuries of knee were 94.7% and 92.1% repectively. 2. Shape, contour of torn meniscus and the relationships between the torn fragments were better demonstrated with HRCT. 3. Horizontal tear could also be suspected by HRCT. 4. Peripheral detachment could be differentiated with synovial recess on the HRCT, especially in the reformat view, but was more clearly visible in arthrography. 5. Arthrography and HRCT can be used as a good complementary study for the evaluation of meniscal injuries.
Arthrography ; Arthroscopy ; Compensation and Redress ; Diagnosis ; Humans ; Knee ; Magnetic Resonance Imaging ; Pathology ; Tears

No abstract available.
Debridement* ; Knee* ; Osteoarthritis* ; Osteotomy*

No abstract available.
Intervertebral Disc* ; Zygapophyseal Joint*

OBJECTIVE: We describe a systematic review and meta-analysis of the performance of 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) for detecting metastasis in ovarian cancer. METHODS: MEDLINE and Embase were searched for diagnostic accuracy studies that used 18F-FDG PET or PET/CT for pre-treatment staging, using surgical findings as the reference standard. Sensitivities and specificities were pooled and plotted in a hierarchic summary receiver operating characteristic plot. Potential causes of heterogeneity were explored through sensitivity analyses. RESULTS: Eight studies with 594 patients were included. The overall pooled sensitivity and specificity for metastasis were 0.72 (95% confidence interval [CI]=0.61–0.81) and 0.93 (95% CI=0.85–0.97), respectively. There was considerable heterogeneity in sensitivity (I2=97.57%) and specificity (I2=96.74%). In sensitivity analyses, studies that used laparotomy as the reference standard showed significantly higher sensitivity and specificity (0.77; 95% CI=0.67–0.87 and 0.96; 95% CI=0.92–0.99, respectively) than those including diagnostic laparoscopy (0.62; 95% CI=0.46–0.77 and 0.84; 95% CI=0.69–0.99, respectively). Higher specificity was shown in studies that confirmed surgical findings by pathologic evaluation (0.95; 95% CI=0.90–0.99) than in a study without pathologic confirmation (0.69; 95% CI=0.24–1.00). Studies with a lower prevalence of the FDG-avid subtype showed higher specificity (0.97; 95% CI=0.94–1.00) than those with a greater prevalence (0.89; 95% CI=0.80–0.97). CONCLUSION: Pre-treatment 18F-FDG PET/CT shows moderate sensitivity and high specificity for detecting metastasis in ovarian cancer. With its low false-positive rate, it can help select surgical approaches or alternative treatment options.
Electrons* ; Fluorodeoxyglucose F18 ; Humans ; Laparoscopy ; Laparotomy ; Neoplasm Metastasis* ; Ovarian Neoplasms* ; Population Characteristics ; Positron-Emission Tomography ; Positron-Emission Tomography and Computed Tomography ; Prevalence ; ROC Curve ; Sensitivity and Specificity

No abstract available.
Catheters ; Humans ; Stroke

No abstract available.
Methylmethacrylate*

In aging societies, incidences of neurodegenerative diseases such as Alzheimer’s disease and Parkinson’s disease are increasing. Neurodegenerative diseases are bringing main challenges to the healthcare system in today’s world. Analyzing characteristic imaging patterns of patients with neurodegenerative diseases is important. Since objective and reliable imaging assessments and precise analyses can lead to early diagnosis of neurodegenerative diseases, imaging patterns are being increasingly investigated. Artificial intelligence (AI) analyzing brain MRI has been applied to neurodegenerative diseases, providing added value in early diagnosis. MRI-based AI software has been developed and studied worldwide, with some AI-based software already being used in actual clinical care. Currently, there are MRI-based volumetry and segmentation software available. There is also an unmet demand for the application of AI in neurodegenerative diseases. Here, we review current status and unmet needs for application of AI in neurodegenerative diseases. We also discuss current limitations of AI, suggestion for AI-based software, and how it can be clinically applied in the future.

Objective: To evaluate the adherence of large language model (LLM)-based healthcare research to the Minimum Reporting Items for Clear Evaluation of Accuracy Reports of Large Language Models in Healthcare (MI-CLEAR-LLM) checklist, a framework designed to enhance the transparency and reproducibility of studies on the accuracy of LLMs for medical applications. Materials and Methods: A systematic PubMed search was conducted to identify articles on LLM performance published in high-ranking clinical medicine journals (the top 10% in each of the 59 specialties according to the 2023 Journal Impact Factor) from November 30, 2022, through June 25, 2024. Data on the six MI-CLEAR-LLM checklist items: 1) identification and specification of the LLM used, 2) stochasticity handling, 3) prompt wording and syntax, 4) prompt structuring, 5) prompt testing and optimization, and 6) independence of the test data—were independently extracted by two reviewers, and adherence was calculated for each item. Results: Of 159 studies, 100% (159/159) reported the name of the LLM, 96.9% (154/159) reported the version, and 91.8% (146/159) reported the manufacturer. However, only 54.1% (86/159) reported the training data cutoff date, 6.3% (10/159) documented access to web-based information, and 50.9% (81/159) provided the date of the query attempts. Clear documentation regarding stochasticity management was provided in 15.1% (24/159) of the studies. Regarding prompt details, 49.1% (78/159) provided exact prompt wording and syntax but only 34.0% (54/159) documented prompt-structuring practices. While 46.5% (74/159) of the studies detailed prompt testing, only 15.7% (25/159) explained the rationale for specific word choices. Test data independence was reported for only 13.2% (21/159) of the studies, and 56.6% (43/76) provided URLs for internet-sourced test data. Conclusion Although basic LLM identification details were relatively well reported, other key aspects, including stochasticity, prompts, and test data, were frequently underreported. Enhancing adherence to the MI-CLEAR-LLM checklist will allow LLM research to achieve greater transparency and will foster more credible and reliable future studies.