Utility of Radiographic Parameter in Assessing Bone Density and Subsequent Fractures in Patients With Osteoporotic Vertebral Compression Fracture.

OBJECTIVE: To investigate the ability of radiological parameter canal bone ratio (CBR) to assess bone mineral density and to differentiate between patients with primary and multiple osteoporotic vertebral compression fracture (OVCF). METHODS: A retrospective analysis was conducted on OVCF patients treated at our hospital. CBR was measured through full-spine x-rays. Patients were categorized into primary and multiple fracture groups. Receiver operating characteristic curve analysis and area under the curve (AUC) calculation were used to assess the ability of parameters to predict osteoporosis and multiple fractures. Predictors of T values were analyzed by multiple linear regression, and independent risk factors for multiple fractures were determined by multiple logistic regression analysis. RESULTS: CBR showed a moderate negative correlation with dual-energy x-ray absorptiometry T values (r = -0.642, p < 0.01). Higher CBR (odds ratio [OR], -6.483; 95% confidence interval [CI], -8.234 to -4.732; p < 0.01) and lower body mass index (OR, 0.054; 95% CI, 0.023-0.086; p < 0.01) were independent risk factors for osteoporosis. Patients with multiple fractures had lower T values (mean ± standard deviation [SD]: -3.76 ± 0.73 vs. -2.83 ± 0.75, p < 0.01) and higher CBR (mean ± SD: 0.54 ± 0.07 vs. 0.46 ± 0.06, p < 0.01). CBR had an AUC of 0.819 in predicting multiple fractures with a threshold of 0.53. T values prediction had an AUC of 0.816 with a threshold of -3.45. CBR > 0.53 was an independent risk factor for multiple fractures (OR, 14.66; 95% CI, 4.97-43.22; p < 0.01). CONCLUSION: CBR is negatively correlated with bone mineral density (BMD) and can be a novel opportunistic BMD assessment method. It is a simple and effective measurement index for predicting multiple fractures, with predictive performance not inferior to T values.

Evaluation of Upper Airway Growth Patterns With Cephalometric Analysis Based on Cervical Vertebral Maturation in the Japanese: A Cross-Sectional Study.

Objective This study aims to establish standard values for the upper airway cross-sectional area and evaluate growth patterns using the cervical vertebral maturation stage (CVMS) in a Japanese population. Methods A cross-sectional sample of 400 patients, aged 6-20 years, was selected randomly from the Orthodontic Clinic at Tokyo Medical and Dental University (TMDU) dental hospital. Cervical vertebral maturation stages (CVMS I-V) guided the classification of participants into five equal groups. Lateral cephalometric radiographs taken prior to orthodontic treatment were used to measure the upper airway's cross-sectional area. The growth spurt and sex differences in growth patterns were assessed through these measurements. Results Standard values for the upper airway dimensions at each CVMS stage were established. Significant growth spurts were noted between CVMS II-III and CVMS III-IV in males and at CVMS II-III in females. The weighted kappa coefficient (κ) demonstrated almost perfect intra- and inter-evaluator agreement, confirming the reliability of CVMS in growth assessment. Conclusion CVMS provides a reliable framework for assessing growth patterns of the upper airway, with distinct variations between sexes noted. These findings support the utility of CVMS in clinical growth evaluation and orthodontic treatment planning.

Quality Assessment of Periapical Radiographs Taken by Dental Assistants Using the Recent Faculty of General Dental Practice (FGDP) Guidelines.

BACKGROUND: Periapical radiographs play a pivotal role in dentistry, offering invaluable insights essential for various dental procedures. OBJECTIVE: This study aims to systematically assess the quality of intraoral periapical (IOPA) radiographs evaluating adherence to the recent guidelines established by the Faculty of General Dental Practice (FGDP). METHODS: A cross-sectional study was conducted at the University College of Dentistry (UCD), employing a non-probability consecutive sampling technique to acquire a calculated sample of 300 IOPA radiographs from the operative, oral surgery, and oral radiology departments. Two senior faculty members evaluated the radiographs according to the recent two-tier grading system outlined in the FGDP guidelines. RESULTS: The study revealed that 197 (65.67%) of the assessed radiographs were diagnostically acceptable, while 103 (34.33%) were deemed diagnostically unacceptable. Contrast problems emerged as the most prevalent issue, accounting for 85 (28.3%) of the cases. Other common problems included incorrect film positioning in 66 (22%), incorrect vertical cone angulation in 37 (12.3%), incorrect horizontal cone angulation in 11 (3.7%), and incorrect processing in 15 (5%) of the IOPA radiographs. CONCLUSION: This study revealed that approximately two-thirds of the IOPA radiographs were deemed diagnostically acceptable. However, contrast issues emerged as the predominant concern affecting image quality. These findings highlight the critical importance of continuous quality improvement initiatives in radiographic practices to enhance diagnostic precision and ensure optimal patient care.

Radiographic findings in dogs with 360 degrees gastric dilatation and volvulus.

Gastric dilatation and volvulus (GDV) is a life-threatening emergency that requires urgent intervention. Radiographic features associated with 360-GDV in dogs have not been investigated. The aim of this retrospective observational study is to describe radiographic features and clinical variables in dogs affected with 360-GDV and to report agreement rates between different radiologists. We also report the sensitivity and specificity of radiographs to diagnose 360-GDV in dogs. Confirmed 360-GDV cases were retrieved, and the radiographic findings were compared with dogs presenting with gastric dilatation (GD) and 180-GDV. Images were reviewed and graded by three blinded board-certified radiologists. A total of 16 dogs with confirmed 360-GDV were identified. The median age was 10 years old (2-13 years). The sensitivity for detection of 360-GDV ranged between 43.7% and 50%, and the specificity between 84.6% and 92.1%. Interobserver agreement on final diagnosis was substantial (Kappa = 0.623; 0.487-0.760, 95% CI). The highest agreement rate was in cases of 180-GDV (87%), followed by the GD cases (72%) and 360-GDV (46%). Severe esophageal distension and absence of small intestinal dilation were the only radiographic features specifically associated with 360-GDV. A similar pyloric position was found between GD and 360-GDV. Additional radiographic variables that could help differentiate GD from 360-GDV include the degree of gastric distension and the peritoneal serosal contrast. Two cases with 360-GDV were misdiagnosed by the three radiologists as GD. In conclusion, radiographically, 360-GDV cases can reassemble GD and vice versa. Radiologists and clinicians should be aware of the low sensitivity of radiographs for the detection of 360-GDV.

To pass or not to pass? Determining the acceptability of anteroposterior and lateral knee radiographs.

INTRODUCTION: The criteria for determining the acceptability of total knee replacement (TKR) radiographs are not established in current clinical practice. In TKR patients, the implant components replaced the anatomical landmarks, making it more difficult for radiographers to determine the degree of rotation. This study aims to establish an acceptable range of knee rotation for TKR radiographs. METHODS: Rejected TKR radiographs (199 AP and 186 lateral) were analysed retrospectively. Radiographers objectively measured rotation on the radiographs. A subset of 46 AP and 46 lateral radiographs were rated by orthopaedic surgeons for rotation and diagnostic value. Inter-rater reliability (IRR) of radiographic measurements and surgeons' ratings were analysed using Bland-Altman and Cohen's kappa, respectively. Spearman's rank-order correlation and Receiver Operator Characteristic analyses were used to determine the correlation and diagnostic performance of the radiographic measurements against the surgeon's ratings. RESULTS: Strong IRR was observed for the radiographic measurements. Only slight to fair agreement was observed for the surgeons' rotation and diagnostic value ratings of the radiographs. Moderate to strong correlation was observed between the radiographic measurements and the surgeons' ratings. The radiographic measurements provided acceptable to excellent discrimination of acceptable and unacceptable radiographs. The acceptable range of measured rotation for usability was AP: 0-5.29 mm and lateral: 0-6.01 mm. CONCLUSION: The proposed measurement methods and the established rotation range could potentially be used by radiographers in clinical practice to determine the acceptability of TKR radiographs. Follow-up studies could investigate uncommon knee implants and seek consensus across different institutions on the acceptable degree of rotation. IMPLICATIONS FOR PRACTICE: The proposed method suggests that accepting radiographs within the threshold (AP: 5.29 mm, lateral: 6.01 mm) reduces repeated examination and radiation exposure and improves imaging efficiency.

Diagnostic Accuracy of Artificial Intelligence for Approximal Caries on Bitewing Radiographs: A Systematic Review and Meta-analysis.

OBJECTIVES: This systematic review and meta-analysis aimed to investigate the diagnostic accuracy of Artificial Intelligence (AI) for approximal carious lesions on bitewing radiographs. METHODS: This study included randomized controlled trials (RCTs) and non-randomized controlled trials (non-RCTs) reporting on the diagnostic accuracy of AI for approximal carious lesions on bitewing radiographs. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. A systematic search was conducted on November 4, 2023, in PubMed, Cochrane, and Embase databases and an updated search was performed on August 28, 2024. The primary outcomes assessed were sensitivity, specificity, and overall accuracy. Sensitivity and specificity were pooled using a bivariate model. RESULTS: Of the 2,442 studies identified, 21 met the inclusion criteria. The pooled sensitivity and specificity of AI were 0.94 (confidence interval (CI): ± 0.78-0.99) and 0.91 (CI: ± 0.84-0.95), respectively. The positive predictive value (PPV) ranged from 0.15 to 0.87, indicating a moderate capacity for identifying true positives among decayed teeth. The negative predictive value (NPV) ranged from 0.79 to 1.00, demonstrating a high ability to exclude healthy teeth. The diagnostic odds ratio was high, indicating strong overall diagnostic performance. CONCLUSIONS: AI models demonstrate clinically acceptable diagnostic accuracy for approximal caries on bitewing radiographs. Although AI can be valuable for preliminary screening, positive findings should be verified by dental experts to prevent unnecessary treatments and ensure timely diagnosis. AI models are highly reliable in excluding healthy approximal surfaces. CLINICAL SIGNIFICANCE: AI can assist dentists in detecting approximal caries on bitewing radiographs. However, expert supervision is required to prevent iatrogenic damage and ensure timely diagnosis.

High accuracy in lower limb alignment analysis using convolutional neural networks, with improvements needed for joint-level metrics.

OBJECTIVE: Evaluation of long-leg standing radiographs (LSR) is a standardised procedure for analysis of primary or secondary deformities of the lower limbs. Deep-learning convolutional neural networks (CNN) offer the potential to enhance radiological measurement by increasing reproducibility and accuracy. This study aims to evaluate the measurement accuracy of an automated CNN-based planning tool (mediCAD® 7.0; mediCAD Hectec GmbH) of lower limb deformities. METHODS: In a retrospective single-centre study, 164 pre- and postoperative bilateral LSRs with uni- or bilateral posttraumatic knee arthritis undergoing total knee arthroplasty (TKA) were enroled. Alignment parameters relevant to knee arthroplasty and deformity correction were analysed independently by two observers and a CNN. The intraclass correlation coefficient (ICC) was used to evaluate the accuracy between observers and the CNN, which was further evaluated using absolute deviations, limits of agreement (LoA) and root mean square error (RMSE). RESULTS: CNN evaluation demonstrated high consistency in measuring leg length (ICC > 0.99) and overall lower limb alignment measures of mechanical tibio-femoral angle (mTFA) (ICC > 0.97; RMSE < 1.1°). The mean absolute difference between angular measurements were low for overall lower limb alignment (mTFA 0.49-0.61°) and high for specific joint angles (aMPFA 3.86-4.50°). Accuracy at specific joint angles like the mechanical proximal tibial angle (MPTA) and the mechanical lateral distal femur angle (mLDFA) varied between lower limbs with deformity, with and without TKA with greatest difference for TKA (ICC 0.22-0.85; RMSE 1.72-3.65°). CONCLUSION: Excellent accuracy was observed between manual and automated measurements for overall alignment and leg length, but joint-level metrics need further improvement especially in case of TKA similar to other existing algorithms. Despite the observed deviations, the time-efficient nature of the algorithm improves the efficiency of the preoperative planning process. LEVEL OF EVIDENCE: IV.

Prevalence and Pattern of Mandibular Condensing Osteitis Lesions in Saudi Population at Qassim Region.

Background: Condensing osteitis (CO) is a common radiopaque lesion observed in the jaws, often detected incidentally on panoramic radiographs. Understanding the prevalence and characteristics of CO is essential for early detection and appropriate management. Objective: To determine the prevalence and characteristics of condensing osteitis among the Saudi population in the Qassim region. Methods: A retrospective study was conducted using 876 digital panoramic radiographs. The presence of CO was identified based on specific radiographic features, and data were collected regarding gender, age, lesion localization, lesion shape, and associated dental status. Results: The prevalence of CO was found to be 2.3% (n = 20) in the study population, with a higher predilection in females (1.4%) compared to males (0.9%). The most commonly affected age group was 30-39 years for males and 10-19 and 30-39 years for females. The mandibular molar region was predominantly affected (90%), with a 'U' shape observed in 55% of the lesions. Root canal treatment was the most commonly associated dental status (75%), followed by deep caries (20%) and large restorations (5%). Conclusion: The study highlights a 2.3% prevalence of CO in the Saudi population of the Qassim region, with a higher predilection in females and a predominant localization in the mandibular molar region. Dental practitioners should be vigilant in identifying CO, especially in at-risk populations, to facilitate timely diagnosis and appropriate management.

Incidence and Pattern of Mandibular Impacted 3rd Molars and Their Relationship with the Mandibular Canal in Population of Jizan Province of Saudi Arabia: A Retrospective Radiographic Study.

Introduction: Impacted mandibular third molars pose challenges in dental practice, often requiring surgical intervention. This retrospective study aims to analyze the demographics, impaction patterns, and anatomical relationships of impacted mandibular third molars among Saudi patients. Methodology: Data from 722 patients visiting the Department of Maxillofacial Surgery and Diagnostic Sciences at Jizan University were retrospectively analyzed. Parameters including gender distribution, impaction types, relationship with the mandibular canal, and age demographics were evaluated based on panoramic radiographs. Results: Bilateral impaction predominated (57.59%), with mesioangular impaction being the most common (46.51%). Gender differences were noted in impaction types and relationships with the mandibular canal. Interruption of white lines of the canal was more frequent in males (70.00%). Early adulthood (20-25 years) exhibited the highest prevalence of impaction. Conclusion: The study provides insights into the demographics and characteristics of impacted mandibular third molars among Saudi patients. Gender-specific variations and age distribution underscore the importance of tailored treatment approaches and early intervention.

Enhanced Osteoporosis Detection Using Artificial Intelligence: A Deep Learning Approach to Panoramic Radiographs with an Emphasis on the Mental Foramen.

Osteoporosis, a skeletal disorder, is expected to affect 60% of women aged over 50 years. Dual-energy X-ray absorptiometry (DXA) scans, the current gold standard, are typically used post-fracture, highlighting the need for early detection tools. Panoramic radiographs (PRs), common in annual dental evaluations, have been explored for osteoporosis detection using deep learning, but methodological flaws have cast doubt on otherwise optimistic results. This study aims to develop a robust artificial intelligence (AI) application for accurate osteoporosis identification in PRs, contributing to early and reliable diagnostics. A total of 250 PRs from three groups (A: osteoporosis group, B: non-osteoporosis group matching A in age and gender, C: non-osteoporosis group differing from A in age and gender) were cropped to the mental foramen region. A pretrained convolutional neural network (CNN) classifier was used for training, testing, and validation with a random split of the dataset into subsets (A vs. B, A vs. C). Detection accuracy and area under the curve (AUC) were calculated. The method achieved an F1 score of 0.74 and an AUC of 0.8401 (A vs. B). For young patients (A vs. C), it performed with 98% accuracy and an AUC of 0.9812. This study presents a proof-of-concept algorithm, demonstrating the potential of deep learning to identify osteoporosis in dental radiographs. It also highlights the importance of methodological rigor, as not all optimistic results are credible.

Comparison of Dental Findings with Computed Tomographic and Clinical Examination in Patients with End-Stage Heart Failure.

Background: This study aimed to evaluate the diagnostic value of pre-existing computed tomography (CT) examinations for the detection of dental pathologies compared with clinical dental examination in patients with end-stage heart failure. Methods: For this purpose, 59 patients with end-stage heart failure and pre-existing non-dental CT images of the craniofacial region were included. Virtual orthopantomograms (vOPG) were reconstructed. Dental pathologies were analyzed in vOPG and source-CT images. Imaging and clinical findings less than 6 months apart were compared (n = 24). Results: The subjective image quality of vOPG was more often rated as insufficient than CT (66%; 20%; p < 0.01). Depending on examination (CT, vOPG or clinic), between 33% and 92% of the patients could require dental intervention such as treatment of caries and periodontitis or tooth extraction. vOPG led to a higher (80%) prevalence of teeth requiring treatment than CT (39%; p < 0.01). The prevalence of teeth requiring treatment was similar in CT (29%) and clinic (29%; p = 1.00) but higher in vOPG (63%; p < 0.01). CT (stage 3 or 4: 42%) and vOPG (38%) underestimated the stage of periodontitis (clinic: 75%; p < 0.01). Conclusions: In conclusion, available CT images including the craniofacial region from patients with end-stage heart failure may contain valuable information regarding oral health status. The assessability of vOPGs might be insufficient and must be interpreted with caution.

Enhanced Diagnostic Accuracy for Dental Caries and Anomalies in Panoramic Radiographs Using a Custom Deep Learning Model.

Background  Dental caries is one of the most prevalent conditions in dentistry worldwide. Early identification and classification of dental caries are essential for effective prevention and treatment. Panoramic dental radiographs are commonly used to screen for overall oral health, including dental caries and tooth anomalies. However, manual interpretation of these radiographs can be time-consuming and prone to human error. Therefore, an automated classification system could help streamline diagnostic workflows and provide timely insights for clinicians. Methods This article presents a deep learning-based, custom-built model for the binary classification of panoramic dental radiographs. The use of histogram equalization and filtering methods as preprocessing techniques effectively addresses issues related to irregular illumination and contrast in dental radiographs, enhancing overall image quality. By incorporating three separate panoramic dental radiograph datasets, the model benefits from a diverse dataset that improves its training and evaluation process across a wide range of caries and abnormalities. Results The dental radiograph analysis model is designed for binary classification to detect the presence of dental caries, restorations, and periapical region abnormalities, achieving accuracies of 97.01%, 81.63%, and 77.53%, respectively. Conclusions The proposed algorithm extracts discriminative features from dental radiographs, detecting subtle patterns indicative of tooth caries, restorations, and region-based abnormalities. Automating this classification could assist dentists in the early detection of caries and anomalies, aid in treatment planning, and enhance the monitoring of dental diseases, ultimately improving and promoting patients' oral healthcare.

Clinical and histological efficacy of a new implant surface in achieving early and stable osseointegration: An in vivo study.

An advantage of treated implant surfaces is their increased degree of hydrophilicity and wettability compared with untreated, machined, smooth surfaces that are hydrophobic. The present preclinical in vivo study aimed to compare the two implant surface types, namely SLActive (Straumann, Basel, Switzerland) and nanohydroxyapatite (Hiossen, Englewood Cliffs, NJ, USA), in achieving early osseointegration. The authors hypothesised that the nanohydroxyapatite surface is comparable to SLActive for early bone-implant contact. Six male mixed foxhounds underwent mandibular premolar and first molar extraction, and the sockets healed for 42 days. The mandibles were randomised to receive implants with either SLActive (control group) or nanohydroxyapatite surfaces (test group). A total of 36 implants were placed in 6 animals, and they were sacrificed at 2 weeks (2 animals), 4 weeks (2 animals) and 6 weeks (2 animals) after implant surgery. When radiographic analysis was performed, the difference in bone level between the two groups was statistically significant at 4 weeks (P = 0.024) and 6 weeks (P = 0.008), indicating that the crestal bone level was better maintained for the test group versus the control group. The bone-implant contact was also higher for the test group at 2 (P = 0.012) and 4 weeks (P = 0.011), indicating early osseointegration. In conclusion, this study underscored the potential of implants with nanohydroxyapatite surfaces to achieve early osseointegration.

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).

Automated Screening of Hip X-rays for Osteoporosis by Singh's Index Using Machine Learning Algorithms.

Introduction: Osteoporosis is a significant and growing global public health problem, projected to increase in the next decade. The Singh Index (SI) is a simple, semi-quantitative evaluation tool for diagnosing osteoporosis with plain hip radiographs based on the visibility of the trabecular pattern in the proximal femur. This work aims to develop an automated tool to diagnose osteoporosis using SI of hip radiograph images with the help of machine learning algorithms. Methods: We used 830 hip X-ray images collected from Indian men and women aged between 20 and 70 which were annotated and labeled for appropriate SI. We employed three state-of-the-art machine learning algorithms-Vision Transformer (ViT), MobileNet-V3, and a Stacked Convolutional Neural Network (CNN)-for image pre-processing, feature extraction, and automation. Each algorithm was evaluated and compared for accuracy, precision, recall, and generalization capabilities to diagnose osteoporosis. Results: The ViT model achieved an overall accuracy of 62.6% with macro-averages of 0.672, 0.597, and 0.622 for precision, recall, and F1 score, respectively. MobileNet-V3 presented a more encouraging accuracy of 69.6% with macro-averages for precision, recall, and F1 score of 0.845, 0.636, and 0.652, respectively. The stacked CNN model demonstrated the strongest performance, achieving an accuracy of 93.6% with well-balanced precision, recall, and F1-score metrics. Conclusion: The superior accuracy, precision-recall balance, and high F1-scores of the stacked CNN model make it the most reliable tool for screening radiographs and diagnosing osteoporosis using the SI.

Artificial Intelligence-Guided Assessment of Femoral Neck Fractures in Radiographs: A Systematic Review and Multilevel Meta-Analysis.

Artificial Intelligence (AI) is a dynamic area of computer science that is constantly expanding its practical benefits in various fields. The aim of this study was to analyze AI-guided radiological assessment of femoral neck fractures by performing a systematic review and multilevel meta-analysis of primary studies. The study protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO) on May 21, 2024 [CRD42024541055]. The updated Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines were strictly followed. A systematic literature search of PubMed, Web of Science, Ovid (Med), and Epistemonikos databases was conducted until May 31, 2024. Critical appraisal using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool showed that the overall quality of the included studies was moderate. In addition, publication bias was presented in funnel plots. A frequentist multilevel meta-analysis was performed using a random effects model with inverse variance and restricted maximum likelihood heterogeneity estimator with Hartung-Knapp adjustment. The accuracy between AI-based and human assessment of femoral neck fractures, sensitivity and specificity with 95% confidence intervals (CIs) were calculated. Study heterogeneity was assessed using the Higgins test I2 (low heterogeneity <25%, moderate heterogeneity: 25%-75%, and high heterogeneity >75%). Finally, 11 studies with a total of 21,163 radiographs were included for meta-analysis. The results of the study quality assessment using the QUADAS-2 tool are presented in Table 2. The funnel plots indicated a moderate publication bias. The AI showed excellent accuracy in assessment of femoral neck fractures (Accuracy = 0.91, 95% CI 0.83 to 0.96; I2 = 99%; p < 0.01). The AI showed good sensitivity in assessment of femoral neck fractures (Sensitivity = 0.87, 95% CI 0.77 to 0.93; I2 = 98%; p < 0.01). The AI showed excellent specificity in assessment of femoral neck fractures (Specificity = 0.91, 95% CI 0.77 to 0.97; I2 = 97%; p < 0.01). AI-guided radiological assessment of femoral neck fractures showed excellent accuracy and specificity as well as good sensitivity. The use of AI as a faster and more reliable assessment tool and as an aid in radiological routine seems justified.

Deep Learning-Based Joint Effusion Classification in Adult Knee Radiographs: A Multi-Center Prospective Study.

Knee effusion, a common and important indicator of joint diseases such as osteoarthritis, is typically more discernible on magnetic resonance imaging (MRI) scans compared to radiographs. However, the use of radiographs for the early detection of knee effusion remains promising due to their cost-effectiveness and accessibility. This multi-center prospective study collected a total of 1413 radiographs from four hospitals between February 2022 to March 2023, of which 1281 were analyzed after exclusions. To automatically detect knee effusion on radiographs, we utilized a state-of-the-art (SOTA) deep learning-based classification model with a novel preprocessing technique to optimize images for diagnosing knee effusion. The diagnostic performance of the proposed method was significantly higher than that of the baseline model, achieving an area under the receiver operating characteristic curve (AUC) of 0.892, accuracy of 0.803, sensitivity of 0.820, and specificity of 0.785. Moreover, the proposed method significantly outperformed two non-orthopedic physicians. Coupled with an explainable artificial intelligence method for visualization, this approach not only improved diagnostic performance but also interpretability, highlighting areas of effusion. These results demonstrate that the proposed method enables the early and accurate classification of knee effusions on radiographs, thereby reducing healthcare costs and improving patient outcomes through timely interventions.

Comparative evaluation of the accuracy of six different apex locators in working length determination of molars using intraoral periapical radiographs: An in vivo study.

Context: One of the crucial steps in endodontic treatment is determining the working length (WL). There are various methods for performing this procedure, one of which is an electronic apex locator (EAL) measurement. Aims: The aim of this study was to determine the accuracy of six EALs, i.e.. Root ZX, Root ZX Mini, Propex PiXi, Innvopex-1, Woodpex III, and Raypex 6 for WL estimation in the mandibular first molars. Material and Method: The study included 180 root canals with symptomatic irreversible pulpitis, divided into six groups using different apex locators. WL determination was compared with intraoral periapical radiographs. Results were categorized as accurate, short, or long. The data were statistically analyzed. Results: ROOT ZX had an accuracy of 96.7%, Root ZX Mini had an accuracy of 93.3%, PiXi had an accuracy of 90.0%, Innvopex-1 had an accuracy of 90.0%, Woodpex III had an accuracy of 86.7%, and Raypex 6 had an accuracy of 83.4%, respectively. There was a statistically nonsignificant difference between groups (P < 0.05). Conclusion: Newly developed apex locators, such as the Innvopex-1, have shown accuracy comparable to well-established EALs like the Root ZX. This highlights the importance of conducting more extensive, large-scale research to confirm and validate their effectiveness.

Transfer Learning-Based Classifier to Automate the Extraction of False X-Ray Images From Hospital's Database.

BACKGROUND: During preclinical training, dental students take radiographs of acrylic (plastic) blocks containing extracted patient teeth. With the digitisation of medical records, a central archiving system was created to store and retrieve all x-ray images, regardless of whether they were images of teeth on acrylic blocks, or those from patients. In the early stage of the digitisation process, and due to the immaturity of the data management system, numerous images were mixed up and stored in random locations within a unified archiving system, including patient record files. Filtering out and expunging the undesired training images is imperative as manual searching for such images is problematic. Hence the aim of this stidy was to differentiate intraoral images from artificial images on acrylic blocks. METHODS: An artificial intelligence (AI) solution to automatically differentiate between intraoral radiographs taken of patients and those taken of acrylic blocks was utilised in this study. The concept of transfer learning was applied to a dataset provided by a Dental Hospital. RESULTS: An accuracy score, F1 score, and a recall score of 98.8%, 99.2%, and 100%, respectively, were achieved using a VGG16 pre-trained model. These results were more sensitive compared to those obtained initally using a baseline model with 96.5%, 97.5%, and 98.9% accuracy score, F1 score, and a recall score respectively. CONCLUSIONS: The proposed system using transfer learning was able to accurately identify "fake" radiographs images and distinguish them from the real intraoral images.

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.