UNASSIGNED: To investigate the relationship between measures of radiographic joint space width (JSW) loss and magnetic resonance imaging (MRI)-based cartilage thickness loss in the medial weight-bearing region of the tibiofemoral joint over 12-24 months. To stratify this relationship by clinically meaningful subgroups (sex and pain status).
UNASSIGNED: We analyzed a subset of knees (n ​= ​256) from the Osteoarthritis Initiative (OAI) likely in early stage OA based on joint space narrowing (JSN) measurements. Natural logarithm transformation was used to approximate near normal distributions for JSW loss. Pearson Correlation coefficients described the relationship between ln-transformed JSW loss and several versions of deep learning-derived MRI-based cartilage thickness loss parameters (minimum, maximum, and mean) in subregions of the femoral condyle, tibial plateau, and combined femoral and tibial regions. Linear mixed-effects models evaluated the associations between the ln-transformed radiographic and MRI-derived measures including potential confounders.
UNASSIGNED: We found weak correlations between ln-transformed JSW loss and MRI-based cartilage thickness ranging from R ​= ​-0.13 (p ​= ​0.20) to R ​= ​0.26 (p ​< ​0.01). Correlations were higher (still poor) among females compared to males and painful compared to non-painful knees. Model results showed weak associations for nearly all MRI-based measures, ranging from no association to β (95% CI) ​= ​0.25 (0.11, 0.39). Associations were higher among females compared to males and minimal differences between painful and non-painful knees.
UNASSIGNED: Despite its recommended use in disease-modifying OA drug clinical trials, results suggest that JSW loss is an ineffective proxy measure of cartilage thickness loss over 12-24 months and within a localized region of the tibiofemoral joint.

暂无摘要。

UNASSIGNED: Axillary radiographs enable the measurement of glenoid retroversion, which is associated with worsened clinical outcomes and glenoid loosening following total shoulder arthroplasty. Due to the variability in radiographic technique, this study aims to determine if the accuracy of retroversion measured by axillary radiograph is affected by 1) scapular rotation and/or 2) proper visualization of the medial scapula.
UNASSIGNED: Using five cadaveric scapulae, investigators obtained axillary radiographs in true neutral position as well as in 10° and 20° of anterior and posterior rotation. For each radiograph, two fellowship trained shoulder surgeons measured glenoid retroversion with complete visualization of the scapula (Technique 1) and with visualization limited to the lateral half of scapula (Technique 2). The observers also measured glenoid retroversion by CT scan to use as a gold standard technique. Spearman\'s Rho was used to assess agreement between measurements.
UNASSIGNED: Average glenoid retroversion of the five scapulae assessed by CT scan was 3.8° (R: 1.5-6.9). Measurements obtained using Technique 1 demonstrated improved levels of interobserver agreement (ICC: 0.412) compared to measurements obtained with Technique 2, which demonstrated no agreement (ICC: 0.103). Scapular rotation was inconsistently associated with agreement using both techniques.
UNASSIGNED: The reliability of glenoid retroversion measurements was limited by incomplete visualization of the medial scapular spine. When measuring retroversion to the base of the scapular spine, improved agreement and accuracy were seen with various degrees of scapular rotation.

Knee osteoarthritis (OA) affects over 650 million patients worldwide. Total knee replacement is aimed at end-stage OA to relieve symptoms of pain, stiffness and reduced mobility. However, the role of imaging modalities in monitoring symptomatic disease progression remains unclear. This study aimed to compare machine learning (ML) models, with and without imaging features, in predicting the two-year Western Ontario and McMaster Universities Arthritis Index (WOMAC) score for knee OA patients. We included 2408 patients from the Osteoarthritis Initiative (OAI) database, with 629 patients from the Multicenter Osteoarthritis Study (MOST) database. The clinical dataset included 18 clinical features, while the imaging dataset contained an additional 10 imaging features. Minimal Clinically Important Difference (MCID) was set to 24, reflecting meaningful physical impairment. Clinical and imaging dataset models produced similar area under curve (AUC) scores, highlighting low differences in performance AUC < 0.025). For both clinical and imaging datasets, Gradient Boosting Machine (GBM) models performed the best in the external validation, with a clinically acceptable AUC of 0.734 (95% CI 0.687-0.781) and 0.747 (95% CI 0.701-0.792), respectively. The five features identified included educational background, family history of osteoarthritis, co-morbidities, use of osteoporosis medications and previous knee procedures. This is the first study to demonstrate that ML models achieve comparable performance with and without imaging features.

OBJECTIVE: To improve reporting and comparability as well as to reduce bias in dental computer vision studies, we aimed to develop a Core Outcome Measures Set (COMS) for this field. The COMS was derived consensus based as part of the WHO/ITU/WIPO Global Initiative AI for Health (WHO/ITU/WIPO AI4H).
METHODS: We first assessed existing guidance documents of diagnostic accuracy studies and conducted interviews with experts in the field. The resulting list of outcome measures was mapped against computer vision modeling tasks, clinical fields and reporting levels. The resulting systematization focused on providing relevant outcome measures whilst retaining details for meta-research and technical replication, displaying recommendations towards (1) levels of reporting for different clinical fields and tasks, and (2) outcome measures. The COMS was consented using a 2-staged e-Delphi, with 26 participants from various IADR groups, the WHO/ITU/WIPO AI4H, ADEA and AAOMFR.
RESULTS: We assigned agreed levels of reporting to different computer vision tasks. We agreed that human expert assessment and diagnostic accuracy considerations are the only feasible method to achieve clinically meaningful evaluation levels. Studies should at least report on eight core outcome measures: confusion matrix, accuracy, sensitivity, specificity, precision, F-1 score, area-under-the-receiver-operating-characteristic-curve, and area-under-the-precision-recall-curve.
CONCLUSIONS: Dental researchers should aim to report computer vision studies along the outlined COMS. Reviewers and editors may consider the defined COMS when assessing studies, and authors are recommended to justify when not employing the COMS.
CONCLUSIONS: Comparing and synthesizing dental computer vision studies is hampered by the variety of reported outcome measures. Adherence to the defined COMS is expected to increase comparability across studies, enable synthesis, and reduce selective reporting.

The reduction of the pulpal space following the deposition of secondary dentin is a radiographically visible morphological feature associated with aging. Currently, there is no reference morphological sample for the Northern Brazilian population when it comes to the radiographically visible dental features for age estimation. This study aimed to test an existing method for age estimation based on the canine pulp/tooth area (PTA) ratio and develop a population-specific equation. The sample consisted of 100 peri-apical radiographs of Brazilian males (n=46) and females (n=54) from the Northern geographic region. The age of the sampled participants was between 18 and 72 years (mean age=45.43±14.39years). The estimated age was obtained with the Cameriere\'s method. A statistically significant negative (r=-0.595) association was observed between the permanent canine PTA and the chronological age (P=0.0001). A population-specific equation was structured with a 4-fold (75%×25%) cross-validation, leading to a mean absolute error of 9.59years, and root mean square error of 11.66years (r2=0.363). This study provided evidence to support the use of Cameriere\'s pulp/tooth area ratio for the radiographic dental age estimation of Northern Brazilian adults, especially adjusted with a population-specific equation.

Patients with low bone mineral density (BMD) face an increased risk of fractures, yet are frequently undiagnosed. Consequently, it is imperative to have opportunistically screen for low BMD in patients undergoing other medical evaluations. This retrospective study encompassed 422 patients aged ≥ 50 who underwent both dual-energy X-ray absorptiometry (DXA) and hand radiographs (modality of digital X-ray) from three different vendors within a 12-month period. The dataset was randomly divided into training/validation (n=338) and test (n=84) datasets. we sought to predict osteoporosis/osteopenia and establish correlations between bone textural analysis and DXA measurements. Our results demonstrate that the deep learning model achieved an accuracy of 77.38%, sensitivity of 77.38%, specificity of 73.63%, and an area under the curve (AUC) of 83% in detecting osteoporosis/osteopenia. These findings suggest that hand radiographs can serve as a viable screening tool for identifying individuals warranting formal DXA assessment for osteoporosis/osteopenia.

OBJECTIVE: This study aimed (I) to test the Willems\' dental age estimation method in different geographic samples of the Brazilian population, and (II) to propose a new model combining the geographic samples in a single reference table of Brazilian maturity scores.
METHODS: The sample consisted of 5017 panoramic radiographs of Brazilian males (n = 2443) and females (n = 2574) between 6 and 15.99 years (mean age = 10.99 ± 2.76 years). The radiographs were collected from the Southeastern (SE) (n = 2920), Central-Western (CW) (n = 1176), and Southern (SO) (n = 921) geographic regions. Demirjian\'s technique was applied followed by Willems\' method and the proposed Brazilian model.
RESULTS: Willems\' method led to mean absolute errors (MAE) of 0.79 and 0.81 years for males and females, respectively. Root mean squared errors (RMSE) were 1.01 and 1.03 years, respectively. The Brazilian model led to MAE of 0.72 and 0.74 years for males and females, respectively, and RMSE of 0.93 years for both sexes. The MAE was reduced in 70% of the age categories. Differences between regions were statistically (p < 0.05) but not clinically significant.
CONCLUSIONS: The new model based on a combined population had an enhanced performance compared to Willems\' model and led to reference outcomes for Brazilians.
CONCLUSIONS: Assessing patients\' biological development by means of dental analysis is relevant to plan orthopedic treatments and follow up. Having a combined-region statistic model for dental age estimation of Brazilian children contributes to optimal age estimation practices.

暂无摘要。

UNASSIGNED: Pediatric scaphoid fractures can be challenging to diagnose on plain radiograph. Rates of missed scaphoid fractures can be as high as 30% to 37% on initial imaging and overall sensitivity ranging from 21% to 97%. Few studies, however, have examined the reliability of radiographs in the diagnosis of scaphoid fractures, and none are specific to the pediatric population. Reliability, both between different specialists and for individual raters, may elucidate some of the diagnostic challenges.
UNASSIGNED: We conducted a 2-iteration survey of pediatric orthopedic surgeons, plastic surgeons, radiologists, and emergency physicians at a tertiary children\'s hospital. Participants were asked to assess 10 series of pediatric wrist radiographs for evidence of scaphoid fracture. Inter-rater and intrarater reliability was calculated using the intraclass correlation coefficient of 2.1.
UNASSIGNED: Forty-two respondents were included in the first iteration analysis. Inter-rater reliability between surgeons (0.66; 95% confidence interval, 0.43-0.87), radiologists (0.76; 0.55-0.92), and emergency physicians (0.65; 0.46-0.86) was \"good\" to \"excellent.\" Twenty-six respondents participated in the second iteration for intrarater reliability (0.73; 0.67-0.78). Sensitivity (0.75; 0.69-0.81) and specificity (0.78; 0.71-0.83) of wrist radiographs for diagnosing scaphoid fractures were consistent with results in other studies.
UNASSIGNED: Both inter-rater and intrarater reliability for diagnosing pediatric scaphoid fractures on radiographs was good to excellent. No significant difference was found between specialists. Plain radiographs, while useful for obvious scaphoid fractures, are unable to reliably rule out subtle fractures routinely. Our study demonstrates that poor sensitivity stems from the test itself, and not rater variability.