The influence of tibial length on radiographic posterior tibial slope measurement: How much tibia do we need?

PURPOSE: The purpose of this study was to determine whether significant differences exist when comparing posterior tibial slope (PTS) measured using increasing lengths of the tibia to determine the anatomical axis. METHODS: Patients with full-length weight-bearing tibial radiographs were retrospectively identified from 2014 to 2022 at a single institution. Patients were excluded if there was any previous history of lower extremity fracture or osteotomy. The anatomical axis of the tibia was determined using the full length of tibial radiographs, and the "reference PTS" was measured using this axis. Using the same radiograph, the PTS was measured using four different anatomical axes at standardized tibial lengths. While the center of the proximal circle remained constant at 5-cm below the tibial plateau, the center of the distal circle was drawn at four points: a) overlapping circles; b) 10-cm distal to the tibial plateau; c) 15-cm distal to the tibial plateau; d) half the length of the tibia, measured from the tibial plateau to the tibial plafond. Bivariate correlation and frequency distribution analysis (measurements >2-degrees from reference PTS) were performed between the reference PTS and PTS measured at each of the four other lengths. RESULTS: A total of 154 patients (39.8 ± 17.4 years old, 44.2% male) were included in the final analysis. Measurements at each of the four tibial lengths were all significantly different from the reference PTS (p < 0.001). The correlation strength improved with increasing tibial length (overlapping: R = 0.681, 10-cm: R = 0.821, 15-cm: R = 0.937, and half-tibia: R = 0.963). The number of PTS measurements >2-degree absolute difference from the reference PTS decreased with increasing tibial length (overlapping: 40.3%, 10-cm: 24.0%, 15-cm: 26.0%, and half-tibia: 18.8%). CONCLUSION: Assessment of PTS is dependent on the length of the tibia utilized to obtain the anatomical axis. Accuracy and precision of PTS measurements improved with increasing length of tibia used to determine the anatomical axis. STUDY DESIGN: Case series.

Automatic classification and grading of canine tracheal collapse on thoracic radiographs by using deep learning.

Tracheal collapse is a chronic and progressively worsening disease; the severity of clinical symptoms experienced by affected individuals depends on the degree of airway collapse. Cutting-edge automated tools are necessary to modernize disease screening using radiographs across various veterinary settings, such as animal clinics and hospitals. This is primarily due to the inherent challenges associated with interpreting uncertainties among veterinarians. In this study, an artificial intelligence model was developed to screen canine tracheal collapse using archived lateral cervicothoracic radiographs. This model can differentiate between a normal and collapsed trachea, ranging from early to severe degrees. The you-only-look-once (YOLO) models, including YOLO v3, YOLO v4, and YOLO v4 tiny, were used to train and test data sets under the in-house XXX platform. The results showed that the YOLO v4 tiny-416 model had satisfactory performance in screening among the normal trachea, grade 1-2 tracheal collapse, and grade 3-4 tracheal collapse with 98.30% sensitivity, 99.20% specificity, and 98.90% accuracy. The area under the curve of the precision-recall curve was >0.8, which demonstrated high diagnostic accuracy. The intraobserver agreement between deep learning and radiologists was κ = 0.975 (P < .001), with all observers having excellent agreement (κ = 1.00, P < .001). The intraclass correlation coefficient between observers was >0.90, which represented excellent consistency. Therefore, the deep learning model can be a useful and reliable method for effective screening and classification of the degree of tracheal collapse based on routine lateral cervicothoracic radiographs.

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.

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.

Traditional radiography versus computed tomography to assess reduced distal radius fractures.

INTRODUCTION: This study compares computed tomography (CT) with plain radiography in its ability to assess distal radius fracture (DRF) malalignment after closed reduction and cast immobilization. METHODS: Malalignment is defined as radiographic fracture alignment beyond threshold values according to the Dutch guideline encompassing angulation, inclination, positive ulnar variance and intra-articular step-off or gap. After identifying 96 patients with correct alignment on initial post-reduction radiographs, we re-assessed alignment on post-reduction CT scans. RESULTS: Significant discrepancies were found between radiographs and CT scans in all measurement parameters. Notably, intra-articular step-off and gap variations on CT scans led to the reclassification of the majority of cases from correct alignment to malalignment. CT scans showed malalignment in 53% of cases, of which 73% underwent surgery. CONCLUSION: When there is doubt about post-reduction alignment based on radiograph imaging, additional CT scanning often reveals malalignment, primarily due to intra-articular incongruency.

Radiographic Edema Is a Predictor of de Quervain's Tenosynovitis.

Background de Quervain's tenosynovitis (DeQ) is a clinical diagnosis; however, due to the symptom overlap with other pathologies, it can occasionally be challenging to make an accurate diagnosis, especially for nonorthopaedic trained physicians. Questions/Purposes We hypothesized that the ratio of radial-sided to ulnar-sided soft tissue swelling could serve as a universally accessible diagnostic tool to assist in differentiating DeQ from other upper extremity conditions. Patients and Methods We retrospectively identified patients with isolated DeQ (M65.4), thumb carpometacarpal arthritis (M18.X), or carpal tunnel syndrome (G56.0x) between 2018 and 2019. Five blinded, independent reviewers evaluated anterior-posterior radiographs of the affected wrist. A digital caliper was used to measure the shortest distance from the lateral cortex of the distal radius and the medial cortex of the distal ulna to the outer edge of the radial and ulnar soft tissue shadows, respectively. Results The mean radial:ulnar ratio in the DeQ group was significantly larger than in the control groups. The interclass correlation coefficient showed strong agreement between all measurements. Patients with a radial:ulnar ratio of 1.7 or higher had a 61% chance of having DeQ with a 56.5% sensitivity, 66.3% specificity, 59.3% positive predictive value (PPV), and 63.8% negative predictive value. A ratio of more than 2.5 correlates to a 55% chance of having DeQ with a sensitivity of 12.9%, specificity of 96.9%, and PPV of 78.6%. Conclusion The ratio of radial- to ulnar-sided wrist edema can be used as a novel diagnostic aid in DeQ, especially for those not trained in orthopaedics or hand surgery. Level of Evidence Level IV, diagnostic study.

Reliability assessment of leg length and angular alignment on manual reads versus artificial intelligence-generated lower extremity radiographic measurements.

PURPOSE: Leg length discrepancy (LLD) and lower extremity malalignment can lead to pain and osteoarthritis. A variety of radiographic parameters are used to assess LLD and alignment. A 510(k) FDA approved artificial intelligence (AI) software locates landmarks on full leg standing radiographs and performs several measurements. The objective of this study was to assess the reliability of this AI tool compared to three manual readers. METHODS: A sample of 320 legs was used. Three readers' measurements were compared to AI output for 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). Intraclass correlation coefficients (ICCs) and Bland-Altman analysis were used to track performance. RESULTS: AI output was successfully produced for 272/320 legs in the study. The reader versus AI pairwise ICCs were mostly in the excellent range: 12/13, 12/13, and 9/13 variables were in the excellent range (ICC > 0.75) for readers 1, 2, and 3, respectively. There was better agreement for leg length, femur length, tibia length, LLD, and HKA than for other variables. The median reading times for the three readers and AI were 250, 282, 236, and 38 s, respectively. CONCLUSION: This study showed that AI-based software provides reliable assessment of LLD and lower extremity alignment with substantial time savings.

Prevalence of Radiographic Hip Dysplasia in the General Adult Population: A Systematic Review.

Background: Acetabular dysplasia has a wide range of prevalence reported in the literature. This variation is likely due to differences in the population under investigation and studies focusing on cohorts with hip pain and osteoarthritis. There are reports of radiographic hip dysplasia prevalence for adults without hip pain but there is no systematic review of these studies to document the incidence in the general population. The purpose of this systematic review was to provide a full summary of all studies that report prevalence of hip dysplasia in adults without hip pain. Methods: PRISMA guidelines were utilized as an outline for this systematic review. Articles were pulled from PubMed, OVID Medline, Embase, SCOPUS, Cochrane Central Register of Clinical Trials, and clinicaltrials.gov from their inception dates to 1/7/24. Studies were included if participants were asymptomatic and reported rates of prevalence. Results: Fourteen studies were included in this systematic review. There were 10,998 hips from 5,506 participants included in this analysis. The overall prevalence of radiographic hip dysplasia was 2.3%. Eight studies of 5,930 hips reported the prevalence of hip dysplasia by sex. The prevalence rate in these studies was 3.8% in females and 2.7% in males. Conclusion: Acetabular dysplasia based on radiographic measurements is relatively common in the general adult population. Furthermore, females have a higher prevalence rate when compared to males. It is important to recognize the incidence of hip dysplasia in the asymptomatic adult population as we recommend surgical treatment for patients who present with hip pain and dysplasia. Further studies should investigate the natural history of untreated and treated hip dysplasia. Level of Evidence: III.

Interpretation time efficiency with radiographs: a comparison study between standard 6 and 12 MP high-resolution display monitors.

Purpose: The purpose of this study is to compare interpretation efficiency of radiologists reading radiographs on 6 megapixel (MP) versus 12 MP monitors. Approach: Our method compares two sets of monitors in two phases: in phase I, radiologists interpreted using a 6 MP, 30.4 in. (Barco Coronis Fusion) and in phase II, a 12 MP, 30.9 in. (Barco Nio Fusion). Nine chest and three musculoskeletal radiologists each batch interpreted an average of 115 radiographs in phase I and 115 radiographs in phase II as a part of routine clinical work. Radiologists were blinded to monitor resolution. Results: Interpretation times per radiograph were noted from dictation logs. Interpretation time was significantly decreased utilizing a 12 MP monitor by 6.88 s ( p = 0.002 ) and 6.76 s (8.7%) ( p < 0.001 ) for chest radiographs only and combined chest and musculoskeletal radiographs, respectively. When evaluating musculoskeletal radiographs alone, the improvement in reading times with 12 MP monitor was 6.76 s, however, this difference was not statistically significant ( p = 0.111 ). Interpretation of radiographs on 12 MP monitors was 8.7% faster than on 6 MP monitors. Conclusion: Higher resolution diagnostic displays can enable radiologists to interpret radiographs more efficiently.

Utility of Recovery Room vs Post-Operative Day 1 Radiographs Following Shoulder Arthroplasty.

Background: Postoperative radiographs may be performed on different timelines after shoulder arthroplasty. Radiographs obtained in the post-operative recovery unit (PACU) are often of poorer quality. The purpose of the current study was to explore and compare the quality of PACU radiographs and radiographs performed in the radiology suite on post-operative Day 1 (POD1), as well as determine their impact on changes in post-operative management. Methods: Our series included 50 consecutive anatomic total shoulder arthroplasties (TSA) for which post-operative radiographs were obtained in the PACU and 50 consecutive TSA for which post-operative radiographs were obtained in the radiology suite on POD 1. TSA radiographs were blinded and reviewed by 3 authors and graded on their quality using criteria described using previously published methods. The weighted kappa was used to describe the intra-rater agreement and inter-rater agreement between two raters. Results: There was no difference in age, sex, BMI, and comorbidities between cohorts. Intra-observer reliability was moderate to substantial with weighted kappa values of 0.65±0.07 (p<0.001), 0.58±0.09 (p<0.001), and 0.67±0.07 (p<0.001). Inter-observer reliability was moderate to substantial with weighted kappa values of 0.605±0.07 (p<0.001), 0.66±0.07 (p<0.001), and 0.65±0.08 (p<0.001). When assessing quality of radiographs, 30% of radiographs obtained in PACU were deemed quality while 57% of radiographs obtained in the radiology suite were deemed quality (p<0.001). Conclusion: Post-operative radiographs in the PACU do not alter patient management and are often inadequate to serve as baseline radiographs. Conversely, radiographs obtained in the radiology suite are of higher quality and can serve as a superior baseline radiograph. Level of Evidence: IV.

The Trochlear Bisector as a New Landmark for Kinematic Alignment in Total Knee Arthroplasty: A Radiographic Study.

Background: In recent years, there has been considerable interest in prosthetic alignment techniques for total knee arthroplasty (TKA), particularly in the so-called kinematic alignment, which aims to restore the knee's native alignment. However, implementing this technique requires specialized instruments and procedural steps that can be laborious. This study introduces the bisector of the trochlear groove as a reliable landmark for performing the distal femoral cut while maintaining parallelism with the native femoral joint line. Methods: Three orthopedic specialists assessed 110 X-ray images of full-leg, weight-bearing lower limbs obtained from healthy individuals between January 2021 and December 2022. The bisector of the trochlear groove was identified on the X-ray images, and the angle between this bisector and the femoral joint line was measured. The consistency of these measurements across repeated assessments and different examiners was evaluated. Results: The bisector of the trochlear groove was found to be perpendicular to the femoral joint line, with a mean angle of 89.4°. The inter-rater reliability was 68% within ±1.3° from the mean, while the intra-rater reliability was 82% within ±1.5° from the mean. Conclusions: These results suggest that by performing a femoral cut perpendicular to the bisector of the trochlear groove, surgeons can inherently restore the femoral joint line of the native knee in patients where the native joint line is no longer identifiable due to the effect of osteoarthritis. This method may offer a viable and straightforward alternative to the standard surgical technique currently practiced for kinematic alignment in TKA.

Bone Metabolism and Dental Implant Insertion as a Correlation Affecting on Marginal Bone Remodeling: Texture Analysis and the New Corticalization Index, Predictor of Marginal Bone Loss-3 Months of Follow-Up.

Background/Objectives: The general condition of implantology patients is crucial when considering the long- and short-term survival of dental implants. The aim of the research was to evaluate the correlation between the new corticalization index (CI) and patients' condition, and its impact on marginal bone loss (MBL) leading to implant failure, using only radiographic (RTG) images on a pixel level. Method: Bone near the dental implant neck was examined, and texture features were analyzed. Statistical analysis includes analysis of simple regression where the correlation coefficient (CC) and R2 were calculated. Detected relationships were assumed to be statistically significant when p < 0.05. Statgraphics Centurion version 18.1.12 (Stat Point Technologies, Warrenton, VA, USA) was used to conduct the statistical analyses. Results: The research revealed a correlation between MBL after 3 months and BMI, PTH, TSH, Ca2+ level in blood serum, phosphates in blood serum, and vitamin D. A correlation was also observed between CI and PTH, Ca2+ level in blood serum, vitamin D, LDL, HDL, and triglycerides on the day of surgery. After 3 months of the observation period, CI was correlated with PTH, TSH, Ca2+ level in blood serum, and triglycerides. Conclusion: The results of the research confirm that the general condition of patients corresponds with CI and MBL. A patient's general condition has an impact on bone metabolism around dental implants. Implant insertion should be considered if the general condition of the patient is not stable. However, CI has not yet been fully investigated. Further studies are necessary to check and categorize the impact of corticalization on marginal bone loss near dental implants.

Performance of Artificial Intelligence Models Designed for Automated Estimation of Age Using Dento-Maxillofacial Radiographs-A Systematic Review.

Automatic age estimation has garnered significant interest among researchers because of its potential practical uses. The current systematic review was undertaken to critically appraise developments and performance of AI models designed for automated estimation using dento-maxillofacial radiographic images. In order to ensure consistency in their approach, the researchers followed the diagnostic test accuracy guidelines outlined in PRISMA-DTA for this systematic review. They conducted an electronic search across various databases such as PubMed, Scopus, Embase, Cochrane, Web of Science, Google Scholar, and the Saudi Digital Library to identify relevant articles published between the years 2000 and 2024. A total of 26 articles that satisfied the inclusion criteria were subjected to a risk of bias assessment using QUADAS-2, which revealed a flawless risk of bias in both arms for the patient-selection domain. Additionally, the certainty of evidence was evaluated using the GRADE approach. AI technology has primarily been utilized for automated age estimation through tooth development stages, tooth and bone parameters, bone age measurements, and pulp-tooth ratio. The AI models employed in the studies achieved a remarkably high precision of 99.05% and accuracy of 99.98% in the age estimation for models using tooth development stages and bone age measurements, respectively. The application of AI as an additional diagnostic tool within the realm of age estimation demonstrates significant promise.

Deep Learning Model for Automatic Identification and Classification of Distal Radius Fracture.

Distal radius fracture (DRF) is one of the most common types of wrist fractures. We aimed to construct a model for the automatic segmentation of wrist radiographs using a deep learning approach and further perform automatic identification and classification of DRF. A total of 2240 participants with anteroposterior wrist radiographs from one hospital between January 2015 and October 2021 were included. The outcomes were automatic segmentation of wrist radiographs, identification of DRF, and classification of DRF (type A, type B, type C). The Unet model and Fast-RCNN model were used for automatic segmentation. The DenseNet121 model and ResNet50 model were applied to DRF identification of DRF. The DenseNet121 model, ResNet50 model, VGG-19 model, and InceptionV3 model were used for DRF classification. The area under the curve (AUC) with 95% confidence interval (CI), accuracy, precision, and F1-score was utilized to assess the effectiveness of the identification and classification models. Of these 2240 participants, 1440 (64.3%) had DRF, of which 701 (48.7%) were type A, 278 (19.3%) were type B, and 461 (32.0%) were type C. Both the Unet model and the Fast-RCNN model showed good segmentation of wrist radiographs. For DRF identification, the AUCs of the DenseNet121 model and the ResNet50 model in the testing set were 0.941 (95%CI: 0.926-0.965) and 0.936 (95%CI: 0.913-0.955), respectively. The AUCs of the DenseNet121 model (testing set) for classification type A, type B, and type C were 0.96, 0.96, and 0.96, respectively. The DenseNet121 model may provide clinicians with a tool for interpreting wrist radiographs.

A Deep-Learning-Based Algorithm for Automatic Detection of Perilunate Dislocation in Frontal Wrist Radiographs.

This study proposes a deep-learning algorithm to automatically detect perilunate dislocation on anteroposterior wrist radiographs. A total of 374 annotated radiographs, 345 normal and 29 pathological, were used to train, validate and test two YOLO v8 deep neural models. The first model was used for detecting the carpal region, and the second for segmenting a region between Gilula's second and third arcs. The output of the segmentation model, trained multiple times with varying random initial parameter values and augmentations, was then assigned a probability of being normal or pathological through ensemble averaging. In this dataset, the algorithm achieved an overall F1-score of 0.880: 0.928 in the normal subgroup, with 1.0 precision, and 0.833 in the pathological subgroup with 1.0 recall (or sensitivity), demonstrating that the diagnosis of perilunate dislocation can be improved through automatic analysis of anteroposterior radiographs. Level of evidence: III.

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.

Automatic detection of the third molar and mandibular canal on panoramic radiographs based on deep learning.

PURPOSE: This study aims to develop a deep learning framework for the automatic detection of the position relationship between the mandibular third molar (M3) and the mandibular canal (MC) on panoramic radiographs (PRs), to assist doctors in assessing and planning appropriate surgical interventions. METHODS: Datasets D1 and D2 were obtained by collecting 253 PRs from a hospitals and 197 PRs from online platforms. The RPIFormer model proposed in this study was trained and validated on D1 to create a segmentation model. The CycleGAN model was trained and validated on both D1 and D2 to develop an image enhancement model. Ultimately, the segmentation and enhancement models were integrated with an object detection model to create a fully automated framework for M3 and MC detection in PRs. Experimental evaluation included calculating Dice coefficient, IoU, Recall, and Precision during the process. RESULTS: The RPIFormer model proposed in this study achieved an average Dice coefficient of 92.56 % for segmenting M3 and MC, representing a 3.06 % improvement over the previous best study. The deep learning framework developed in this research enables automatic detection of M3 and MC in PRs without manual cropping, demonstrating superior detection accuracy and generalization capability. CONCLUSION: The framework developed in this study can be applied to PRs captured in different hospitals without the need for model fine-tuning. This feature is significant for aiding doctors in accurately assessing the spatial relationship between M3 and MC, thereby determining the optimal treatment plan to ensure patients' oral health and surgical safety.

Developing Machine Learning-Based Predictive Models for Hallux Valgus Recurrence Based on Measurements From Radiographs.

BACKGROUND: Machine learning (ML) is increasingly used to predict the prognosis of numerous diseases. This retrospective analysis aimed to develop a prediction model using ML algorithms and to identify predictors associated with the recurrence of hallux valgus (HV) following surgery. METHODS: A total of 198 symptomatic feet that underwent chevron osteotomy combined with a distal soft tissue procedure were enrolled and analyzed from 2 independent medical centers. The feet were grouped according to nonrecurrence or recurrence based on 1-year follow-up outcomes. Preoperative weightbearing radiographs and immediate postoperative nonweightbearing radiographs were obtained for each HV foot. Radiographic measurements (eg, HV angle and intermetatarsal angle) were acquired and used for ML model training. A total of 9 commonly used ML models were trained on the data obtained from one institute (108 feet), and tested on the other data set from another independent institute (90 feet) for external validation. Optimal feature sets for each model were identified based on a 2000-resample bootstrap-based internal validation via an exhaustive search. The performance of each model was then tested on the external validation set. The area under the curve (AUC), classification accuracy, sensitivity, and specificity of each model were calculated to evaluate the performance of each model. RESULTS: The support vector machine (SVM) model showed the highest predictive accuracy compared to other methods, with an AUC of 0.88 and an accuracy of 75.6%. Preoperative hallux valgus angle, tibial sesamoid position, postoperative intermetatarsal angle, and postoperative tibial sesamoid position were identified as the most selected features by several ML models. CONCLUSION: ML classifiers such as SVM could predict the recurrence of HV (an HVA >20 degrees) at a 1-year follow-up while identifying associated predictors in a multivariate manner. This study holds the potential for foot and ankle surgeons to effectively identify individuals at higher risk of HV recurrence postsurgery.

Comparison of two CBCT analysis techniques with conventional periapical radiographs in assessment of tertiary dentin after indirect pulp capping in young permanent teeth.

BACKGROUND AND AIM: To compare two cone beam computed tomography (CBCT) analysis techniques for measuring tertiary dentin (TD) volume, density, and root length increase, after indirect pulp therapy (IPT) in young permanent teeth with conventional periapical radiographs. DESIGN: Comparative study design: Sixty-nine CBCT scans were taken initially (T1) and after 1 year (T2) of IPT. New CBCT analysis technique A, standardization, segmentation, and registration of T1 and T2 scans were performed using ITK-SNAP and 3D Slicer CMF to measure TD volume (mm3), density (gray-level intensity), and root length increase (mm). In the traditional CBCT analysis technique B, analyses were conducted using the In-Vivo software to calculate TD thickness (mm), radiodensity (HU%), and root length increase (mm). Paired t-test and the intraclass correlation coefficient were calculated to compare and assess the reliability of all techniques. RESULTS: No significant difference between the two techniques existed in the measurement of TD mineral density (Mean [SD]:A = 22.4 [15.4]; B = 24.4 [15.4]; p = .47). Technique A resulted in significantly higher root length increase values (Mean [SD]: A = 1.3 [0.6]; B = 1.1 [0.5]; p = .03). The two techniques showed acceptable reliability levels (0.76-0.99). CONCLUSION: CBCT analysis techniques yielded similar findings for mineral density. The new CBCT volumetric analysis technique, although more laborious, produced higher values for root length increase, and allowed for measurement of dentin volume.