Multimodal Imaging of Structural Damage and Inflammation in Psoriatic Arthritis: A comparison of DMARD-Naive and DMARD-Failure Patients.

OBJECTIVES: To compare inflammatory and structural differences in active Psoriatic Arthritis (PsA) between disease-modifying antirheumatic drug (DMARD)-naive and DMARD-failure patients using diverse imaging approaches for future analyses. Additionally, to explore the influence of patient characteristics (clinical and demographic variables) on imaging findings. METHODS: Of the 80 patients included from the first cohort of the ongoing multicentre TOFA-PREDICT trial, 40 were DMARD-naive and 40 were DMARD-failure (csDMARD failure; 1 prior bDMARD excluding etanercept was allowed), all meeting classification criteria for PsA with a minimum disease duration of eight weeks. Baseline conventional radiographs of hands and feet, MRIs of both ankles, and whole-body 18F-FDG PET/CT were evaluated for inflammatory and structural imaging parameters, including Sharp-van der Heijde (SHS), Heel Enthesitis Magnetic Resonance Imaging Scoring System (HEMRIS) and Deauville synovitis scoring. Differences between groups and the influence of patient characteristics were examined with multiple linear regression. RESULTS: At baseline, patient characteristics were similar between groups. Imaging parameters showed limited inflammation and structural damage. Inflammatory imaging parameters were not significantly different (p> 0.200). Among structural parameters, only HEMRIS Achilles tendon structural damage was significantly different (p= 0.024, R2=0.071) and, SHS Joint Space Narrowing was not statistically significant (p= 0.050, R2=0.048) with higher values for both in DMARD-failures. After correction of patient characteristics, these differences in imaging disappeared (both p> 0.600). CONCLUSION: At baseline, PsA patient groups were comparable concerning structural and inflammatory imaging parameters, especially after correcting for patient characteristics. Thus, DMARD-naive and DMARD-failure patient groups may be combined in future PsA progression and treatment decision studies. CLINICAL TRIAL REGISTRATION NUMBER: EudraCT: 2017-003900-28.

Using time-series chest radiographs and laboratory data by machine learning for identifying pulmonary infection and colonization of Acinetobacter baumannii.

BACKGROUND: Accurately distinguishing between pulmonary infection and colonization in patients with Acinetobacter baumannii is of utmost importance to optimize treatment and prevent antibiotic abuse or inadequate therapy. An efficient automated sorting tool could prompt individualized interventions and enhance overall patient outcomes. This study aims to develop a robust machine learning classification model using a combination of time-series chest radiographs and laboratory data to accurately classify pulmonary status caused by Acinetobacter baumannii. METHODS: We proposed nested logistic regression models based on different time-series data to automatically classify the pulmonary status of patients with Acinetobacter baumannii. Advanced features were extracted from the time-series data of hospitalized patients, encompassing dynamic pneumonia indicators observed on chest radiographs and laboratory indicator values recorded at three specific time points. RESULTS: Data of 152 patients with Acinetobacter baumannii cultured from sputum or alveolar lavage fluid were retrospectively analyzed. Our model with multiple time-series data demonstrated a higher performance of AUC (0.850, with a 95% confidence interval of [0.638-0.873]), an accuracy of 0.761, a sensitivity of 0.833. The model, which only incorporated a single time point feature, achieved an AUC of 0.741. The influential model variables included difference in the chest radiograph pneumonia score. CONCLUSION: Dynamic assessment of time-series chest radiographs and laboratory data using machine learning allowed for accurate classification of colonization and infection with Acinetobacter baumannii. This demonstrates the potential to help clinicians provide individualized treatment through early detection.

Facial dysmorphology in children exposed in pregnancy to anticonvulsant medications correlates with deficits in IQ.

Some children exposed at conception to the antiepileptic drugs (AEDs) phenytoin (PHT), phenobarbital (PB), and carbamazepine (CBZ) have changes in their midface and fingers. It has been suggested that the anticonvulsant-exposed child with these subtle changes in facial features (the "anticonvulsant face") has a greater likelihood of having deficits in IQ in comparison with children exposed to the same anticonvulsants who do not have these features. 115 AED-exposed children (40, PHT; 34, PB; and 41, CBZ) between 6.5 and 16 years of age and 111 unexposed children matched by sex, race, and year in school were evaluated. The evaluations were (WISC-III), physical examination with measurements of facial features and digits and photographs. The AED-exposed children had cephalometric radiographs, but not the unexposed. Each parent had a similar examination of face and hands plus tests of intelligence. These AED-exposed children showed an increased frequency of a short nose and anteverted nares, features of the "anticonvulsant face." Lateral skull radiographs showed a decrease in the angle between the anterior cranial base and nasal bone, which produces anteverted nares. Mean IQs were significantly lower on one or more IQ measures for the children with these facial features. Shortening of the distal phalanges and small fingernails correlated with the presence of a short nose in that child. The findings in 115 children exposed at conception to either phenytoin, phenobarbital, or carbamazepine, as monotherapy, confirmed the hypothesis that those with a short nose and anteverted nares had a lower IQ than exposed children without those features.

The Cayor Atlas: a black Senegalese dental atlas for permanent tooth formation.

INTRODUCTION: Many aspects of tooth development have been documented, particularly in Caucasian populations. However, dental development has not been extensively studied in West Africa. OBJECTIVE: The present study was designed to provide information on the sequences of tooth calcification in West African black Senegalese children and to compare the results with those of other populations, notably the London Atlas. METHODS: A total of 556 orthopantomograms (OPGs) from 289 males and 266 females with a mean age of 11.34 ± 3.84 years were analyzed. Demirjian A-H staging was applied to record the stages of tooth development. Tables of tooth development stages for each tooth were generated separately for age cohorts and by sex. The most common stage of tooth formation (modal) was the characteristic age stage of development. Differences between boys and girls and between maxillary and mandibular teeth were also analyzed using chi-squares. Accuracy was assessed by comparing the age estimated by the Dental Development Atlas for this population (Cayor Atlas) and the London Atlas tooth with chronological age using the Bland-Altman test. RESULTS: There was no significant difference in tooth development between girls and boys, p > 0.05. Maxillary teeth had similar dental development to mandibular teeth, p > 0.05. The Pearson correlation test showed a strong correlation between chronological age and the age estimated by the Cayor atlas, p < 0.001. The Bland-Altman test also showed greater accuracy than the London Atlas. CONCLUSION: These results show dental calcification sequences different from those of the London Atlas Tooth and the Witts Atlas (Atlas of Black South African Subjects).

Automatic and robust estimation of sex and chronological age from panoramic radiographs using a multi-task deep learning network: a study on a South Korean population.

Sex and chronological age estimation are crucial in forensic investigations and research on individual identification. Although manual methods for sex and age estimation have been proposed, these processes are labor-intensive, time-consuming, and error-prone. The purpose of this study was to estimate sex and chronological age from panoramic radiographs automatically and robustly using a multi-task deep learning network (ForensicNet). ForensicNet consists of a backbone and both sex and age attention branches to learn anatomical context features of sex and chronological age from panoramic radiographs and enables the multi-task estimation of sex and chronological age in an end-to-end manner. To mitigate bias in the data distribution, our dataset was built using 13,200 images with 100 images for each sex and age range of 15-80 years. The ForensicNet with EfficientNet-B3 exhibited superior estimation performance with mean absolute errors of 2.93 ± 2.61 years and a coefficient of determination of 0.957 for chronological age, and achieved accuracy, specificity, and sensitivity values of 0.992, 0.993, and 0.990, respectively, for sex prediction. The network demonstrated that the proposed sex and age attention branches with a convolutional block attention module significantly improved the estimation performance for both sex and chronological age from panoramic radiographs of elderly patients. Consequently, we expect that ForensicNet will contribute to the automatic and accurate estimation of both sex and chronological age from panoramic radiographs.

Efficacy of the methods of age determination using artificial intelligence in panoramic radiographs - a systematic review.

The aim of this systematic review is to analyze the literature to determine whether the methods of artificial intelligence are effective in determining age in panoramic radiographs. Searches without language and year limits were conducted in PubMed/Medline, Embase, Web of Science, and Scopus databases. Hand searches were also performed, and unpublished manuscripts were searched in specialized journals. Thirty-six articles were included in the analysis. Significant differences in terms of root mean square error and mean absolute error were found between manual methods and artificial intelligence techniques, favoring the use of artificial intelligence (p < 0.00001). Few articles compared deep learning methods with machine learning models or manual models. Although there are advantages of machine learning in data processing and deep learning in data collection and analysis, non-comparable data was a limitation of this study. More information is needed on the comparison of these techniques, with particular emphasis on time as a variable.

Accuracy of cone-beam computed tomography in imaging the components of the periodontal phenotype.

The components and dimensions of the periodontal and peri-implant phenotype have a high relevance in contemporary dental research and should be taken into consideration in the decision-making process in the management of a variety of clinical scenarios to optimize the outcomes of therapy. Various assessment methods for quantifying and classifying the phenotypical dimensions have emerged and developed in recent decades. Nevertheless, the use of cone-beam computed tomography (CBCT) scans remains the most commonly used approach worldwide. However, the accuracy to adequately imaging and measuring the dimensions of the hard and soft tissue components around teeth may represent a significant challenge in different clinical scenarios due to factors such as the age of the patient and motion during the scan, presence of metallic artifacts causing streaks and gray-value distortion, overlapping of soft tissue structures, machine performance, file processing, and small voxel size among others. These factors pose a particular challenge when tiny structures are under investigation, for example, the buccal/lingual bony or soft tissue layer of lower/upper incisors. Therefore, this review addresses the underlying technical information of the use of CBCT scans, and suggests some recommendations on the utilization of this method of assessment to optimally use it despite its' system-inherent limitations.

Imaging approach to ingested foreign bodies in the neck.

Foreign body ingestion is a common clinical occurrence worldwide, with high morbidity in the pediatric population and in adult patients with intentional attempts. Coins and button battery ingestions are more common among children. Bone impaction and swallowed dentures are usually seen in older adults. While most ingested foreign bodies pass through the gastrointestinal tract spontaneously with no complications, some require endoscopic and/or surgical intervention. Complications such as pharyngoesophageal ulceration, perforation, stricture, and deep neck infection can develop without timely diagnosis and management. The purpose of this article is to familiarize radiologists with the imaging approach to assess for characteristics and impacted locations of ingested foreign bodies in the neck.

Pediatric chest radiograph interpretation in a real-life setting.

Chest radiography is a frequently used imaging modality in children. However, only fair to moderate inter-observer agreement has been reported between chest radiograph interpreters. Most studies were not performed in real-world clinical settings. Our aims were to examine the agreement between emergency department pediatricians and board-certified radiologists in a pediatric real-life setting and to identify clinical risk factors for the discrepancies. Included were children aged 3 months to 18 years who underwent chest radiography in the emergency department not during the regular hours of radiologist interpretation. Every case was reviewed by an expert panel. Inter-observer agreement between emergency department pediatricians and board-certified radiologists was assessed by Cohen's kappa; risk factors for disagreement were analyzed. Among 1373 cases, the level of agreement between emergency department pediatricians and board-certified radiologists was "moderate" (k = 0.505). For radiographs performed after midnight, agreement was only "fair" (k = 0.391). The expert panel identified clinically relevant disagreements in 260 (18.9%) of the radiographs. Over-treatment of antibiotics was identified in 121 (8.9%) of the cases and under-treatment in 79 (5.8%). In a multivariable logistic regression, the following parameters were found to be significantly associated with disagreements: neurological background (p = 0.046), fever (p = 0.001), dyspnea (p = 0.014), and radiographs performed after midnight (p = 0.007). CONCLUSIONS: Moderate agreement was found between emergency department pediatricians and board-certified radiologists in interpreting chest radiographs. Neurological background, fever, dyspnea, and radiographs performed after midnight were identified as risk factors for disagreement. Implementing these findings could facilitate the use of radiologist expertise, save time and resources, and potentially improve patient care. WHAT IS KNOWN: • Only fair to moderate inter-observer agreement has been reported between chest radiograph interpreters. • Most studies were not performed in real-world clinical settings. Clinical risk factors for disagreements have not been reported. WHAT IS NEW: • In this study, which included 1373 cases at the emergency department, the level of agreement between interpreters was only "moderate." • The major clinical parameters associated with interpretation discrepancies were neurological background, fever, dyspnea, and interpretations conducted during the night shift.

Deep learning model for pleural effusion detection via active learning and pseudo-labeling: a multisite study.

BACKGROUND: The study aimed to develop and validate a deep learning-based Computer Aided Triage (CADt) algorithm for detecting pleural effusion in chest radiographs using an active learning (AL) framework. This is aimed at addressing the critical need for a clinical grade algorithm that can timely diagnose pleural effusion, which affects approximately 1.5 million people annually in the United States. METHODS: In this multisite study, 10,599 chest radiographs from 2006 to 2018 were retrospectively collected from an institution in Taiwan to train the deep learning algorithm. The AL framework utilized significantly reduced the need for expert annotations. For external validation, the algorithm was tested on a multisite dataset of 600 chest radiographs from 22 clinical sites in the United States and Taiwan, which were annotated by three U.S. board-certified radiologists. RESULTS: The CADt algorithm demonstrated high effectiveness in identifying pleural effusion, achieving a sensitivity of 0.95 (95% CI: [0.92, 0.97]) and a specificity of 0.97 (95% CI: [0.95, 0.99]). The area under the receiver operating characteristic curve (AUC) was 0.97 (95% DeLong's CI: [0.95, 0.99]). Subgroup analyses showed that the algorithm maintained robust performance across various demographics and clinical settings. CONCLUSION: This study presents a novel approach in developing clinical grade CADt solutions for the diagnosis of pleural effusion. The AL-based CADt algorithm not only achieved high accuracy in detecting pleural effusion but also significantly reduced the workload required for clinical experts in annotating medical data. This method enhances the feasibility of employing advanced technological solutions for prompt and accurate diagnosis in medical settings.

YOLO-V5 based deep learning approach for tooth detection and segmentation on pediatric panoramic radiographs in mixed dentition.

OBJECTIVES: In the interpretation of panoramic radiographs (PRs), the identification and numbering of teeth is an important part of the correct diagnosis. This study evaluates the effectiveness of YOLO-v5 in the automatic detection, segmentation, and numbering of deciduous and permanent teeth in mixed dentition pediatric patients based on PRs. METHODS: A total of 3854 mixed pediatric patients PRs were labelled for deciduous and permanent teeth using the CranioCatch labeling program. The dataset was divided into three subsets: training (n = 3093, 80% of the total), validation (n = 387, 10% of the total) and test (n = 385, 10% of the total). An artificial intelligence (AI) algorithm using YOLO-v5 models were developed. RESULTS: The sensitivity, precision, F-1 score, and mean average precision-0.5 (mAP-0.5) values were 0.99, 0.99, 0.99, and 0.98 respectively, to teeth detection. The sensitivity, precision, F-1 score, and mAP-0.5 values were 0.98, 0.98, 0.98, and 0.98, respectively, to teeth segmentation. CONCLUSIONS: YOLO-v5 based models can have the potential to detect and enable the accurate segmentation of deciduous and permanent teeth using PRs of pediatric patients with mixed dentition.

Deep learning-based automated bone age estimation for Saudi patients on hand radiograph images: a retrospective study.

PURPOSE: In pediatric medicine, precise estimation of bone age is essential for skeletal maturity evaluation, growth disorder diagnosis, and therapeutic intervention planning. Conventional techniques for determining bone age depend on radiologists' subjective judgments, which may lead to non-negligible differences in the estimated bone age. This study proposes a deep learning-based model utilizing a fully connected convolutional neural network(CNN) to predict bone age from left-hand radiographs. METHODS: The data set used in this study, consisting of 473 patients, was retrospectively retrieved from the PACS (Picture Achieving and Communication System) of a single institution. We developed a fully connected CNN consisting of four convolutional blocks, three fully connected layers, and a single neuron as output. The model was trained and validated on 80% of the data using the mean-squared error as a cost function to minimize the difference between the predicted and reference bone age values through the Adam optimization algorithm. Data augmentation was applied to the training and validation sets yielded in doubling the data samples. The performance of the trained model was evaluated on a test data set (20%) using various metrics including, the mean absolute error (MAE), median absolute error (MedAE), root-mean-squared error (RMSE), and mean absolute percentage error (MAPE). The code of the developed model for predicting the bone age in this study is available publicly on GitHub at https://github.com/afiosman/deep-learning-based-bone-age-estimation . RESULTS: Experimental results demonstrate the sound capabilities of our model in predicting the bone age on the left-hand radiographs as in the majority of the cases, the predicted bone ages and reference bone ages are nearly close to each other with a calculated MAE of 2.3 [1.9, 2.7; 0.95 confidence level] years, MedAE of 2.1 years, RMAE of 3.0 [1.5, 4.5; 0.95 confidence level] years, and MAPE of 0.29 (29%) on the test data set. CONCLUSION: These findings highlight the usability of estimating the bone age from left-hand radiographs, helping radiologists to verify their own results considering the margin of error on the model. The performance of our proposed model could be improved with additional refining and validation.

Changes in femoral anteversion after intramedullary nailing for pediatric femoral shaft fracture: a multicenter study.

BACKGROUND: The rotational change after using a flexible intramedullary (IM) nail for femoral shaft fractures has been a concern for many surgeons. Recently, a statistical shape model (SSM) was developed for the three-dimensional reconstruction of the femur from two-dimensional plain radiographs. In this study, we measured postoperative femoral anteversion (FAV) in patients diagnosed with femoral shaft fractures who were treated with flexible IM nails and investigated age-related changes in FAV using the SSM. METHODS: This study used radiographic data collected from six regional tertiary centers specializing in pediatric trauma in South Korea. Patients diagnosed with femoral shaft fractures between September 2002 and June 2020 and patients aged < 18 years with at least two anteroposterior (AP) and lateral (LAT) femur plain radiographs obtained at least three months apart were included. A linear mixed model (LMM) was used for statistical analysis. RESULTS: Overall, 72 patients were included in the study. The average patient age was 7.6 years and the average follow-up duration was 6.8 years. The average FAV of immediate postoperative images was 27.5 ± 11.5°. Out of 72 patients, 52 patients (72.2%) showed immediate postoperative FAV greater than 20°. The average FAV in patients with initial FAV > 20° was 32.74°, and the LMM showed that FAV decreased by 2.5° (p = 0.0001) with each 1-year increase from the time of initial trauma. CONCLUSIONS: This study explored changes in FAV after femoral shaft fracture using a newly developed technology that allows 3D reconstruction from uncalibrated 2D images. There was a pattern of change on the rotation of the femur after initial fixation, with a 2.5° decrease of FAV per year.

Application of deep learning for automated diagnosis and classification of hip dysplasia on plain radiographs.

BACKGROUND: Hip dysplasia is a condition where the acetabulum is too shallow to support the femoral head and is commonly considered a risk factor for hip osteoarthritis. The objective of this study was to develop a deep learning model to diagnose hip dysplasia from plain radiographs and classify dysplastic hips based on their severity. METHODS: We collected pelvic radiographs of 571 patients from two single-center cohorts and one multicenter cohort. The radiographs were split in half to create hip radiographs (n = 1022). One orthopaedic surgeon and one resident assessed the radiographs for hip dysplasia on either side. We used the center edge (CE) angle as the primary diagnostic criteria. Hips with a CE angle < 20°, 20° to 25°, and > 25° were labeled as dysplastic, borderline, and normal, respectively. The dysplastic hips were also classified with both Crowe and Hartofilakidis classification of dysplasia. The dataset was divided into train, validation, and test subsets using 80:10:10 split-ratio that were used to train two deep learning models to classify images into normal, borderline and (1) Crowe grade 1-4 or (2) Hartofilakidis grade 1-3. A pre-trained on Imagenet VGG16 convolutional neural network (CNN) was utilized by performing layer-wise fine-turning. RESULTS: Both models struggled with distinguishing between normal and borderline hips. However, achieved high accuracy (Model 1: 92.2% and Model 2: 83.3%) in distinguishing between normal/borderline vs. dysplastic hips. The overall accuracy of Model 1 was 68% and for Model 2 73.5%. Most misclassifications for the Crowe and Hartofilakidis classifications were +/- 1 class from the correct class. CONCLUSIONS: This pilot study shows promising results that a deep learning model distinguish between normal and dysplastic hips with high accuracy. Future research and external validation are warranted regarding the ability of deep learning models to perform complex tasks such as identifying and classifying disorders using plain radiographs. LEVEL OF EVIDENCE: Diagnostic level IV.

Deep learning generated lower extremity radiographic measurements are adequate for quick assessment of knee angular alignment and leg length determination.

PURPOSE: Angular and longitudinal deformities of leg alignment create excessive stresses across joints, leading to pain and impaired function. Multiple measurements are used to assess these deformities on anteroposterior (AP) full-length radiographs. An artificial intelligence (AI) software automatically locates anatomical landmarks on AP full-length radiographs and performs 13 measurements to assess knee angular alignment and leg length. The primary aim of this study was to evaluate the agreements in LLD and knee alignment measurements between an AI software and two board-certified radiologists in patients without metal implants. The secondary aim was to assess time savings achieved by AI. METHODS: The measurements assessed in the study were hip-knee-angle (HKA), anatomical-tibiofemoral angle (aTFA), anatomical-mechanical-axis angle (AMA), joint-line-convergence angle (JLCA), mechanical-lateral-proximal-femur-angle (mLPFA), mechanical-lateral-distal-femur-angle (mLDFA), mechanical-medial-proximal-tibia-angle (mMPTA), mechanical-lateral-distal-tibia- angle (mLDTA), femur length, tibia length, full leg length, leg length discrepancy (LLD), and mechanical axis deviation (MAD). These measurements were performed by two radiologists and the AI software on 164 legs. Intraclass-correlation-coefficients (ICC) and Bland-Altman analyses were used to assess the AI's performance. RESULTS: The AI software set incorrect landmarks for 11/164 legs. Excluding these cases, ICCs between the software and radiologists were excellent for 12/13 variables (11/13 with outliers included), and the AI software met performance targets for 11/13 variables (9/13 with outliers included). The mean reading time for the AI algorithm and two readers, respectively, was 38.3, 435.0, and 625.0 s. CONCLUSION: This study demonstrated that, with few exceptions, this AI-based software reliably generated measurements for most variables in the study and provided substantial time savings.

Identification of fractures on pediatric foot radiographs: do localization cues improve diagnostic accuracy and reduce interpretation time?

OBJECTIVE: To investigate the diagnostic accuracy and time in the detection of fractures on pediatric foot radiographs marked without and with localization cues. METHOD: One-hundred randomly selected foot radiographic examinations that were performed on children (<18 years old) after injury and with at least 4 weeks of follow-up were included. Blinded to history and diagnosis, 4 readers (one each: medical student, pediatrician, pediatric orthopedic surgeon, and pediatric musculoskeletal radiologist) retrospectively and independently reviewed each examination twice (without and with cue, at least 1 month apart, and after randomization). Each reader recorded the presence or absence of a fracture, fracture location, diagnostic confidence, and the total (interpretation) time spent on each study. Diagnostic accuracy, reader confidence, and interpretation time were compared between examinations without and with cues. RESULTS: Our study included 59 examinations without and 41 with fractures (21 phalangeal, 18 metatarsal, and 2 tarsal fractures). Localization cues improved inter-reader agreement (κ=0.36 to 0.64), overall sensitivity (68 to 72%), specificity (66 to 73%), and diagnostic accuracy (67 to 73%); thus, overcalled and missed rates also improved from 34 to 27% and 32 to 28%, respectively. Reader confidence improved with cue (49 to 61%, p<0.01) with higher incremental improvement with younger children (30% for 1-6 years; 14% for 7-11 years; and 10% for 12-17 years). Interpretation time decreased by 40% per examination (40±22 s without to 24±13 s with cues, p<0.001). CONCLUSION: Localization cues improved diagnostic accuracy and reader confidence, reducing interpretation time in the detection of pediatric foot fractures.

The multifaceted musculoskeletal hydatid disease.

Musculoskeletal hydatid disease is rare and can be located anywhere but most commonly the bone and muscles of the spine, pelvis, then the lower limbs. Imaging is essential for its diagnosis, performing the pre-therapeutic assessment, guiding possible percutaneous treatments, and providing post-therapeutic follow-up. Musculoskeletal hydatidosis can take several forms that may suggest other infections and tumors or pseudotumors. MRI and CT are superior for its diagnosis but ultrasound and radiography remain the most accessible examinations in developing countries where this parasitosis is endemic. In this review, we provide an overview of this disease and describe its different imaging patterns in soft tissue and bone involvement that should be sought to support the diagnosis.

Artificial intelligence in musculoskeletal imaging: realistic clinical applications in the next decade.

This article will provide a perspective review of the most extensively investigated deep learning (DL) applications for musculoskeletal disease detection that have the best potential to translate into routine clinical practice over the next decade. Deep learning methods for detecting fractures, estimating pediatric bone age, calculating bone measurements such as lower extremity alignment and Cobb angle, and grading osteoarthritis on radiographs have been shown to have high diagnostic performance with many of these applications now commercially available for use in clinical practice. Many studies have also documented the feasibility of using DL methods for detecting joint pathology and characterizing bone tumors on magnetic resonance imaging (MRI). However, musculoskeletal disease detection on MRI is difficult as it requires multi-task, multi-class detection of complex abnormalities on multiple image slices with different tissue contrasts. The generalizability of DL methods for musculoskeletal disease detection on MRI is also challenging due to fluctuations in image quality caused by the wide variety of scanners and pulse sequences used in routine MRI protocols. The diagnostic performance of current DL methods for musculoskeletal disease detection must be further evaluated in well-designed prospective studies using large image datasets acquired at different institutions with different imaging parameters and imaging hardware before they can be fully implemented in clinical practice. Future studies must also investigate the true clinical benefits of current DL methods and determine whether they could enhance quality, reduce error rates, improve workflow, and decrease radiologist fatigue and burnout with all of this weighed against the costs.

Chest radiographic thoracic areas and respiratory outcomes in infants with anterior abdominal wall defects.

OBJECTIVES: Infants with anterior abdominal wall defects (AWD) can suffer from pulmonary complications. Our aims were to determine if the chest radiographic thoracic areas (CRTAs) on day one differed between infants with exomphalos or gastroschisis, whether this related to differing severity of outcomes and if they were lower than those of controls indicating abnormal antenatal lung growth. METHODS: A review of infants with exomphalos or gastroschisis born between January 2004 and January 2023 was conducted. The control group was term, newborn infants ventilated for poor respiratory drive at birth. Chest radiographs on day one were analysed and the highest CRTA in the first 24 h after birth for each infant included in the analysis. RESULTS: The 127 infants with gastroschisis had a lower gestational age and birthweight than the 62 exomphalos infants and 130 controls (all p<0.001) The CRTAs of the controls were greater than the CRTAs of the exomphalos and the gastroschisis infants (p = 0.001). The median CRTA corrected for birthweight was lower in the exomphalos infants [688, IQR 568-875 mm2/kg] than the gastroschisis infants [813, IQE 695-915 mm2/kg] No gastroschisis infant developed bronchopulmonary dysplasia (BPD). A CRTA of 1759 mm2 had a sensitivity of 81 % and specificity of 71 % in predicting BPD in infants with exomphalos. CONCLUSIONS: Infants with gastroschisis or exomphalos had lower CRTAs than controls suggesting both groups had abnormal antenatal lung development. The CRTA was lower in the exomphalos infants who also had worse respiratory outcomes, hence CRTA assessment may a useful prognostic aid.

Weight-bearing pain and implant migration, progressive radiolucencies, radiolucency more than 2 mm and subsidence on radiographs and CT are generally accepted criteria for knee arthroplasty loosening: An international Delphi consensus study.

PURPOSE: Establishing the diagnosis of loosening in total or unicondylar knee arthroplasty remains a challenge with different clinical and radiological signs evaluated in study designs with high risk of bias, where few or incomplete criteria are formulated for establishing the diagnosis of implant loosening. This study aimed at evaluating the variability between different clinical and radiological criteria and establish a consensus regarding clinical and radiological criteria for the diagnosis of knee arthroplasty loosening. METHODS: Highly specialized knee surgeons focusing on revision arthroplasty were invited to take part in an international panel for a Delphi consensus study. In the first round, the participants were asked to state their most important clinical and radiological criteria for implant loosening. In a second round, the panel's agreement with the collected criteria was rated on a 5-point Likert scale (1-5). High variability was defined by receiving at least one score each indicating complete disagreement and complete agreement. Consensus was established when over 70% of participants rated a criterion as 'fully agree' (5) or 'mostly agree' (4). RESULTS: High variability was observed in 56% of clinical criteria and 38% of radiological criteria. A consensus was reached on one clinical (weight-bearing pain [82%]) and four radiological criteria, that is, implant migration, progressive radiolucencies, subsidence and radiolucencies >2 mm on X-ray or computed tomography (CT) (84%-100%). CONCLUSION: Amongst specialized knee revision surgeons, there is high variability in clinical and radiological criteria that are seen as important contributing factors to diagnosis of knee implant loosening. A consensus was reached on weight-bearing pain as clinical criterion and on implant migration, progressive radiolucencies, subsidence and radiolucencies of more than 2 mm on X-ray or CT as radiological criteria. The variability rates observed, along with the criteria that reached consensus, offer important insights for the standardization of diagnostic protocols. LEVEL OF EVIDENCE: Level V.