Functional preoperative assessment of coronal knee laxity better predicts postoperative patient outcomes than intraoperative surgeon-defined laxity in total knee arthroplasty.

PURPOSE: Intraoperative laxity assessments in total knee arthroplasty (TKA) are subjective, with few studies comparing against standardised preoperative and postoperative assessments. This study compares coronal knee laxity in TKA patients awake and anaesthetised, preprosthesis and postprosthesis implantation, evaluating relationships to patient-reported outcome measures. METHODS: A retrospective analysis of 49 TKA joints included preoperative and postoperative computed tomography scans, stress radiographs and knee injury and osteoarthritis outcome score (KOOS) questionnaire results preoperatively and 12 months postoperatively. The imaging was used to assess functional laxity (FL) in awake patients, whereas computer navigation measured intraoperative surgical laxity (SL) preimplantation and postimplantation, with patients anaesthetised. Varus and valgus stress states and their difference, joint laxity, were measured. RESULTS: SL was greater than FL in both preimplantation [8.1° (interquartile range, IQR 2.0°) and 3.8° (IQR 2.9°), respectively] and postimplantation [3.5° (IQR 2.3°) and 2.5° (IQR 2.7°), respectively]. Preimplantation, SL was more likely than FL to categorise knees as correctable to ±3° of the mechanical axis. Preoperative FL correlated with KOOS Symptoms (r = 0.33, p = .02) and quality of life (QOL) (r = 0.38, p = .01), whereas reducing medial laxity with TKA enhanced postoperative QOL outcomes (p = .02). CONCLUSIONS: Functional coronal knee laxity assessment of awake patients is generally lower than intraoperative surgical assessments of anaesthetised patients. Preoperative SL may result in overcorrection of coronal TKA alignment, whereas preoperative FL better predicts postoperative patient outcomes and reflects the patients' native and tolerable knee laxity. Preoperative FL assessment can be used to guide surgical planning. LEVEL OF EVIDENCE: Level II.

Investigating the relationship between radiographic joint space width loss and deep learning-derived magnetic resonance imaging-based cartilage thickness loss in the medial weight-bearing region of the tibiofemoral joint.

Objective: 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). Design: We analyzed a subset of knees (n â€‹= â€‹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. Results: We found weak correlations between ln-transformed JSW loss and MRI-based cartilage thickness ranging from R â€‹= â€‹-0.13 (p â€‹= â€‹0.20) to R â€‹= â€‹0.26 (p â€‹< â€‹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 ß (95% CI) â€‹= â€‹0.25 (0.11, 0.39). Associations were higher among females compared to males and minimal differences between painful and non-painful knees. Conclusions: 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.

Early Detection of Heart Failure with Autonomous AI-Based Model Using Chest Radiographs: A Multicenter Study.

The opportunistic use of radiological examinations for disease detection can potentially enable timely management. We assessed if an index created by an AI software to quantify chest radiography (CXR) findings associated with heart failure (HF) could distinguish between patients who would develop HF or not within a year of the examination. Our multicenter retrospective study included patients who underwent CXR without an HF diagnosis. We included 1117 patients (age 67.6 ± 13 years; m:f 487:630) that underwent CXR. A total of 413 patients had the CXR image taken within one year of their HF diagnosis. The rest (n = 704) were patients without an HF diagnosis after the examination date. All CXR images were processed with the model (qXR-HF, Qure.AI) to obtain information on cardiac silhouette, pleural effusion, and the index. We calculated the accuracy, sensitivity, specificity, and area under the curve (AUC) of the index to distinguish patients who developed HF within a year of the CXR and those who did not. We report an AUC of 0.798 (95%CI 0.77-0.82), accuracy of 0.73, sensitivity of 0.81, and specificity of 0.68 for the overall AI performance. AI AUCs by lead time to diagnosis (<3 months: 0.85; 4-6 months: 0.82; 7-9 months: 0.75; 10-12 months: 0.71), accuracy (0.68-0.72), and specificity (0.68) remained stable. Our results support the ongoing investigation efforts for opportunistic screening in radiology.

Early monitoring of inlay wear after total knee arthroplasty on plain radiographs using model-based wear measurement.

Wear of the ultra-high molecular-weight polyethylene (UHMWPE) component in total knee arthroplasty contributes to implant failure. It is often detected late, when patients experience pain or instability. Early monitoring could enable timely intervention, preventing implant failure and joint degeneration. This study investigates the accuracy and precision (repeatability) of model-based wear measurement (MBWM), a novel technique that can estimate inlay thickness and wear radiographically. Six inlays were milled from non-crosslinked UHMWPE and imaged via X-ray in anteroposterior view at flexion angles 0°, 30°, and 60° on a phantom knee model. MBWM measurements were compared with reference values from a coordinate measurement machine. Three inlays were subjected to accelerated wear generation and similarly evaluated. MBWM estimated inlay thickness with medial and lateral accuracies of 0.13 ± 0.09 and 0.14 ± 0.09 mm, respectively, and linear wear with an accuracy of 0.07 ± 0.06 mm. Thickness measurements revealed significant lateral differences at 0° and 30° (0.22 ± 0.08 mm vs. 0.06 ± 0.06 mm, respectively; t-test, p = 0.0002). Precision was high, with average medial and lateral differences of - 0.01 ± 0.04 mm between double experiments. MBWM using plain radiographs presents a practical and promising approach for the clinical detection of implant wear.

Opportunistic Screening for Osteoporosis Using Hand Radiographs - A Feature Augmentation Study Technique (FAST).

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.

Comparative analysis of deep-learning-based bone age estimation between whole lateral cephalometric and the cervical vertebral region in children.

Bone age determination in individuals is important for the diagnosis and treatment of growing children. This study aimed to develop a deep-learning model for bone age estimation using lateral cephalometric radiographs (LCRs) and regions of interest (ROIs) in growing children and evaluate its performance. This retrospective study included 1050 patients aged 4-18 years who underwent LCR and hand-wrist radiography on the same day at Pusan National University Dental Hospital and Ulsan University Hospital between January 2014 and June 2023. Two pretrained convolutional neural networks, InceptionResNet-v2 and NasNet-Large, were employed to develop a deep-learning model for bone age estimation. The LCRs and ROIs, which were designated as the cervical vertebrae areas, were labeled according to the patient's bone age. Bone age was collected from the same patient's hand-wrist radiograph. Deep-learning models trained with five-fold cross-validation were tested using internal and external validations. The LCR-trained model outperformed the ROI-trained models. In addition, visualization of each deep learning model using the gradient-weighted regression activation mapping technique revealed a difference in focus in bone age estimation. The findings of this comparative study are significant because they demonstrate the feasibility of bone age estimation via deep learning with craniofacial bones and dentition, in addition to the cervical vertebrae on the LCR of growing children.

Deep learning for tubes and lines detection in critical illness: Generalizability and comparison with residents.

Background: Artificial intelligence (AI) has been proven useful for the assessment of tubes and lines on chest radiographs of general patients. However, validation on intensive care unit (ICU) patients remains imperative. Methods: This retrospective case-control study evaluated the performance of deep learning (DL) models for tubes and lines classification on both an external public dataset and a local dataset comprising 303 films randomly sampled from the ICU database. The endotracheal tubes (ETTs), central venous catheters (CVCs), and nasogastric tubes (NGTs) were classified into "Normal," "Abnormal," or "Borderline" positions by DL models with and without rule-based modification. Their performance was evaluated using an experienced radiologist as the standard reference. Results: The algorithm showed decreased performance on the local ICU dataset, compared to that of the external dataset, decreasing from the Area Under the Curve of Receiver (AUC) of 0.967 (95 % CI 0.965-0.973) to the AUC of 0.70 (95 % CI 0.68-0.77). Significant improvement in the ETT classification task was observed after modifications were made to the model to allow the use of the spatial relationship between line tips and reference anatomy with the improvement of the AUC, increasing from 0.71 (95 % CI 0.70 - 0.75) to 0.86 (95 % CI 0.83 - 0.94). Conclusions: The externally trained model exhibited limited generalizability on the local ICU dataset. Therefore, evaluating the performance of externally trained AI before integrating it into critical care routine is crucial. Rule-based algorithm may be used in combination with DL to improve results.

A novel formula for estimating the vertical dimension of occlusion using panoramic radiography.

BACKGROUND: This study aimed to develop a new formula to easily estimate the vertical dimension of occlusion (VDO) by using the distance between the mental foramen on a panoramic radiograph. SUBJECTS AND METHODS: A total of 508 dentulous subjects were selected from outpatient dental clinics at the College of Dental Medicine, Al-Azhar University. The vertical dimension of the occlusion was measured using a single calibrated calliper. For each subject, a digital panoramic radiograph was taken with fixed exposure parameters. The intermental foramina distance (IMFD) was measured. The data were collected and then analysed using the IBM SPSS version 20.0 software package. (Armonk, NY: IBM Corp.). Linear regression was used to determine the relationship between the intermental foramina distance (IMFD) and the vertical dimension at occlusion (VDO). RESULTS: Pearson's correlation analysis revealed that there was a strong correlation between the intermental foramina distance (IMFD) and the VDO. Thus, a novel formula was developed for determining the VDO using panoramic radiography. CONCLUSION: The novel formula developed herein facilitated the determination of the VDO among prosthetic rehabilitation for subjects who lost vertical dimension due to loss of posterior teeth or severe wear of natural posterior teeth. Further studies are needed to determine the clinical applicability of the derived formulae for edentulous subjects.

Morphological dental age estimation of adults using the radiographic canine pulp/tooth area ratio.

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.

Core outcomes measures in dental computer vision studies (DentalCOMS).

OBJECTIVES: 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. CONCLUSION: 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. CLINICAL SIGNIFICANCE: 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.

Assessing the Value of Imaging Data in Machine Learning Models to Predict Patient-Reported Outcome Measures in Knee Osteoarthritis Patients.

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.

Effect of Fixed and Removable Functional Therapy on Mandibular Anterior Bone Structures: A Fractal Analysis Study.

(1) Background and aim: The effects of functional therapies on dentoalveolar and skeletal structures have been investigated in orthodontics for many years. The aim of this retrospective study was to evaluate the changes caused by fixed and removable functional therapy in the mandibular anterior trabecular structures using fractal dimension (FD) analysis. (2) Methods: A total of 60 patients with skeletal and dental class II malocclusion were included in the study and three groups were formed: the untreated control group (CG), the Forsus fatigue-resistant device group (FFRDG), and the Monoblock group (MBG). Bone areas of interest determined in the buccoapical of the mandibular incisors and the symphysis in the lateral cephalometric radiographs taken before (T0) and after (T1) functional therapy were evaluated using FD analysis. The relationship between the FD and IMPA (Incisor Mandibular Plane Angle) angles was evaluated. Parametric and nonparametric tests were used in statistical analysis according to normality distribution. The statistical significance level was determined as p < 0.05. (3) Results: There was no statistically significant difference between the FD values of all groups at T0 (p > 0.05). At T1, buccoapical FD values were significantly lower in FFRDG and MBG compared to the control group (p < 0.05), while symphyseal FD values were not found to be significant (p > 0.05). The IMPA angle was significantly lower in the FFRDG and MBG than in the control group at T0, while it was higher at T1 (p < 0.05). While a significant negative correlation was observed between the IMPA angle and buccoapical FD values in both FFRDG and MBG (p < 0.05), it was not observed with the symphysis FD values (p > 0.05). (4) Conclusions: Trabecular changes caused by functional therapy in the mandibular anterior bone can be evaluated on lateral cephalometric radiographs with FD analysis. It was concluded that orthodontists should ensure controlled changes in the IMPA angle during functional therapy, especially for the decreases in FDs seen in the buccoapical alveolar region due to the forward movement of the mandibular incisors.

Diagnostic Efficiency of Point-Of-Care Lung Ultrasonography in the Diagnosis of Adult Community-Acquired Pneumonia - A Single-Centre Study in Southeastern Nigeria.

BACKGROUND: Lung ultrasonography is an emerging tool in diagnosing community-acquired pneumonia (CAP) - a major cause of mortality worldwide. The objective of the study was to determine the diagnostic performance of point-of-care ultrasound (POCUS) of the lung compared to the chest radiograph in the diagnosis of CAP in adults. METHODS: Adults ≥ 18 years presenting at the general and medical outpatient clinics, medical and emergency wards with symptoms of suspected CAP were evaluated using a portable ultrasound device and single posteroanterior chest radiograph. Sensitivity, specificity, positive and negative predictive values (PPV and NPV), positive and negative likelihood ratios (LR+ and LR-) with corresponding 95% confidence intervals were computed for the lung ultrasound (LUS) against the chest radiograph as the criterion standard. RESULTS: Out of the 65 patients eventually studied, 50 (76.9%) were diagnosed with pneumonia by chest radiograph. The sensitivity, specificity, PPV, NPV, LR+, LR- and DOR for the LUS against the chest radiograph, respectively, were 96% (95%CI, 86.3% - 99.5%), 93.3% (95%CI, 68.1% - 99.8%), 98.0% (95%CI, 87.8% - 99.7%), 87.5% (64.1% - 96.5%), 14.4 (95%CI, 2.2 - 95.7), 0.04 (95%CI, 0.01 - 0.17) and 336 (28.3 - 3985.0). The overall accuracy was 95.4% (95%CI, 87.1 - 99.0%). The median time to completion of the LUS was 13 minutes. CONCLUSION: Lung ultrasound at the point of care is a reasonably accurate tool for the diagnosis of CAP in adults presenting with typical features.

CONTEXTE: L'échographie pulmonaire est un outil émergent dans le diagnostic de la pneumonie communautaire (CAP) ­ une cause majeure de mortalité dans le monde entier. L'objectif de l'étude était de déterminer la performance diagnostique de l'échographie pulmonaire au point de soins (POCUS) par rapport à la radiographie thoracique dans le diagnostic de la CAP chez les adultes. MÉTHODES: Les adultes ≥ 18 ans se présentant aux cliniques générales et médicales, aux services médicaux et d'urgence avec des symptômes de CAP suspectée ont été évalués à l'aide d'un appareil d'échographie portable et d'une radiographie thoracique postéroantérieure unique. La sensibilité, la spécificité, les valeurs prédictives positive et négative (PPV et NPV), les rapports de vraisemblance positifs et négatifs (LR+ et LR-) avec les intervalles de confiance correspondants à 95 % ont été calculés pour l'échographie pulmonaire (LUS) par rapport à la radiographie thoracique comme norme de référence. RÉSULTATS: Sur les 65 patients étudiés, 50 (76,9 %) ont été diagnostiqués avec une pneumonie par adiographie thoracique. La sensibilité, la spécificité, la PPV, la NPV, les LR+, LR- et DOR pour la LUS par rapport à la radiographie thoracique étaient respectivement de 96 % (IC à 95%, 86,3% ­ 99,5%), 93,3% (IC à 95%, 68,1% ­ 99,8%), 98,0% (IC à 95%, 87,8% - 99,7%), 87,5% (64,1% - 96,5%), 14,4 (IC à 95%, 2,2 ­ 95,7), 0,04 (IC à 95 %, 0,01 ­ 0,17) et 336 (28,3 ­ 3985,0). La précision globale était de 95,4 % (IC à 95%, 87,1 ­ 99,0%). Le temps médian pour l'achèvement de la LUS était de 13 minutes. CONCLUSION: L'échographie pulmonaire au point de soins est un outil raisonnablement précis pour le diagnostic de la CAP chez les adultes présentant des caractéristiques typiques. MOTS-CLÉS: Échographie pulmonaire, Radiographie thoracique, Pneumonie communautaire, Précision diagnostique, Ressources limitées.

Two-step deep learning models for detection and identification of the manufacturers and types of dental implants on panoramic radiographs.

The purpose of this study is to develop two-step deep learning models that can automatically detect implant regions on panoramic radiographs and identify several types of implants. A total of 1,574 panoramic radiographs containing 3675 implants were included. The implant manufacturers were Kyocera, Dentsply Sirona, Straumann, and Nobel Biocare. Model A was created to detect oral implants and identify the manufacturers using You Only Look Once (YOLO) v7. After preparing the image patches that cropped the implant regions detected by model A, model B was created to identify the implant types per manufacturer using EfficientNet. Model A achieved very high performance, with recall of 1.000, precision of 0.979, and F1 score of 0.989. It also had accuracy, recall, precision, and F1 score of 0.98 or higher for the classification of the manufacturers. Model B had high classification metrics above 0.92, exception for Nobel's class 2 (Parallel). In this study, two-step deep learning models were built to detect implant regions, identify four manufacturers, and identify implant types per manufacturer.

Coronal plane stability of cruciate-retaining total knee arthroplasty in valgus gonarthrosis patients: A mid-term evaluation using stress radiographs.

BACKGROUND: Total knee arthroplasty (TKA) using implants with a high level of constraint has generally been recommended for patients with osteoarthritis (OA) who have valgus alignment. However, studies have reported favorable outcomes even with cruciate-retaining (CR) implants. AIM: To evaluate the coronal plane stability of CR-TKA in patients with valgus OA at the mid-term follow-up. METHODS: Patients with primary valgus OA of the knee who underwent TKA from January 2014 to January 2021 were evaluated through stress radiography using a digital stress device with 100 N of force on both the medial and lateral side. Gap openings and degrees of angulation change were determined. Descriptive statistical analysis was performed for both continuous and categorical variables. Inter-rater reliability of the radiographic measurements was evaluated using Cronbach's alpha. RESULTS: This study included 25 patients (28 knees) with a mean preoperative mechanical valgus axis of 11.3 (3.6-27.3) degrees. The mean follow-up duration was 3.4 (1.04-7.4) years. Stress radiographs showed a median varus and valgus gap opening of 1.6 (IQR 0.6-3.0) mm and 1.7 (IQR 1.3-2.3) mm and varus and valgus angulation changes of 2.5 (IQR 1.3-4.8) degrees and 2.3 (IQR 2.0-3.6) degrees, respectively. No clinical signs of instability, implant loosening, or revision due to instability were observed throughout this case series. CONCLUSION: The present study demonstrated that using CR-TKA for patients with valgus OA of the knee promoted excellent coronal plane stability.

The Brazilian (FRANCO) method for dental age estimation: Willems' model revisited.

OBJECTIVES: 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. MATERIALS AND 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. CONCLUSION: The new model based on a combined population had an enhanced performance compared to Willems' model and led to reference outcomes for Brazilians. CLINICAL RELEVANCE: 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.

Diagnostic accuracy of chest X-ray and CT using artificial intelligence for osteoporosis: systematic review and meta-analysis.

INTRODUCTION: Artificial intelligence (AI)-based systems using chest images are potentially reliable for diagnosing osteoporosis. METHODS: We performed a systematic review and meta-analysis to assess the diagnostic accuracy of chest X-ray and computed tomography (CT) scans using AI for osteoporosis in accordance with the diagnostic test accuracy guidelines. We included any type of study investigating the diagnostic accuracy of index test for osteoporosis. We searched MEDLINE, EMBASE, the Cochrane Central Register of Controlled Trials, and IEEE Xplore Digital Library on November 8, 2023. The main outcome measures were the sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) for osteoporosis and osteopenia. We described forest plots for sensitivity, specificity, and AUC. The summary points were estimated from the bivariate random-effects models. We summarized the overall quality of evidence using the Grades of Recommendation, Assessment, Development, and Evaluation approach. RESULTS: Nine studies with 11,369 participants were included in this review. The pooled sensitivity, specificity, and AUC of chest X-rays for the diagnosis of osteoporosis were 0.83 (95% confidence interval [CI] 0.75, 0.89), 0.76 (95% CI 0.71, 0.80), and 0.86 (95% CI 0.83, 0.89), respectively (certainty of the evidence, low). The pooled sensitivity and specificity of chest CT for the diagnosis of osteoporosis and osteopenia were 0.83 (95% CI 0.69, 0.92) and 0.70 (95% CI 0.61, 0.77), respectively (certainty of the evidence, low and very low). CONCLUSIONS: This review suggests that chest X-ray with AI has a high sensitivity for the diagnosis of osteoporosis, highlighting its potential for opportunistic screening. However, the risk of bias of patient selection in most studies were high. More research with adequate participants' selection criteria for screening tool will be needed in the future.

Effect of Axillary Radiograph Rotation and Medial Scapular Visualization on Glenoid Retroversion Measurement.

Objectives: 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. Methods: 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. Results: 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. Conclusion: 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.

Estimating lumbar bone mineral density from conventional MRI and radiographs with deep learning in spine patients.

PURPOSE: This study aimed to develop machine learning methods to estimate bone mineral density and detect osteopenia/osteoporosis from conventional lumbar MRI (T1-weighted and T2-weighted images) and planar radiography in combination with clinical data and imaging parameters of the acquisition protocol. METHODS: A database of 429 patients subjected to lumbar MRI, radiographs and dual-energy x-ray absorptiometry within 6 months was created from an institutional database. Several machine learning models were trained and tested (373 patients for training, 86 for testing) with the following objectives: (1) direct estimation of the vertebral bone mineral density; (2) classification of T-score lower than - 1 or (3) lower than - 2.5. The models took as inputs either the images or radiomics features derived from them, alone or in combination with metadata (age, sex, body size, vertebral level, parameters of the imaging protocol). RESULTS: The best-performing models achieved mean absolute errors of 0.15-0.16 g/cm2 for the direct estimation of bone mineral density, and areas under the receiver operating characteristic curve of 0.82 (MRIs) - 0.80 (radiographs) for the classification of T-scores lower than - 1, and 0.80 (MRIs) - 0.65 (radiographs) for T-scores lower than - 2.5. CONCLUSIONS: The models showed good discriminative performances in detecting cases of low bone mineral density, and more limited capabilities for the direct estimation of its value. Being based on routine imaging and readily available data, such models are promising tools to retrospectively analyse existing datasets as well as for the opportunistic investigation of bone disorders.

Automatic measurement of anatomical parameters of the lumbar vertebral body and the intervertebral disc on radiographs by deep learning.

Background: Lumbar spine disorders are one of the common causes of low back pain (LBP). Objective and reliable measurement of anatomical parameters of the lumbar spine is essential in the clinical diagnosis and evaluation of lumbar disorders. However, manual measurements are time-consuming and laborious, with poor consistency and repeatability. Here, we aim to develop and evaluate an automatic measurement model for measuring the anatomical parameters of the vertebral body and intervertebral disc based on lateral lumbar radiographs and deep learning (DL). Methods: A model based on DL was developed with a dataset consisting of 1,318 lateral lumbar radiographs for the prediction of anatomical parameters, including vertebral body heights (VBH), intervertebral disc heights (IDH), and intervertebral disc angles (IDA). The mean of the values obtained by 3 radiologists was used as a reference standard. Statistical analysis was performed in terms of standard deviation (SD), mean absolute error (MAE), Percentage of correct keypoints (PCK), intraclass correlation coefficient (ICC), regression analysis, and Bland-Altman plot to evaluate the performance of the model compared with the reference standard. Results: The percentage of intra-observer landmark distance within the 3 mm threshold was 96%. The percentage of inter-observer landmark distance within the 3 mm threshold was 94% (R1 and R2), 92% (R1 and R3), and 93% (R2 and R3), respectively. The PCK of the model within the 3 mm distance threshold was 94-99%. The model-predicted values were 30.22±3.01 mm, 10.40±3.91 mm, and 10.63°±4.74° for VBH, IDH, and IDA, respectively. There were good correlation and consistency in anatomical parameters of the lumbar vertebral body and disc between the model and the reference standard in most cases (R2=0.89-0.95, ICC =0.93-0.98, MAE =0.61-1.15, and SD =0.89-1.64). Conclusions: The newly proposed model based on a DL algorithm can accurately measure various anatomical parameters on lateral lumbar radiographs. This could provide an accurate and efficient measurement tool for the quantitative evaluation of spinal disorders.