Search Results (118)

Objective: To develop and validate a ResNet-50-based deep learning model for automatic detection of osteomyelitis (DFO) in plain radiographs of patients with diabetic foot ulcers (DFUs). Research Design and Methods: This retrospective study included 168 patients with type one or type two diabetes and clinical suspicion of DFO confirmed via a surgical bone biopsy. An experienced clinician and a pretrained ResNet-50 model independently interpreted the radiographs. The model was developed using Python-based frameworks with ChatGPT assistance for coding. The diagnostic performance was assessed against the histopathological findings, calculating sensitivity, specificity, the positive predictive value (PPV), the negative predictive value (NPV), and the likelihood ratios. Agreement between the AI model and the clinician was evaluated using Cohen’s kappa coefficient. Results: The AI model demonstrated high sensitivity (92.8%) and PPV (0.97), but low-level specificity (4.4%). The clinician showed 90.2% sensitivity and 37.8% specificity. The Cohen’s kappa coefficient between the AI model and the clinician was −0.105 (p = 0.117), indicating weak agreement. Both the methods tended to classify many cases as DFO-positive, with 81.5% agreement in the positive cases. Conclusions: This study demonstrates the potential of IA to support the radiographic diagnosis of DFO using a ResNet-50-based deep learning model. AI-assisted radiographic interpretation could enhance early DFO detection, particularly in high-prevalence settings. However, further validation is necessary to improve its specificity and assess its utility in primary care. Full article

Pneumothorax is a potentially life-threatening condition defined as the collapse of the lung due to air leakage into the chest cavity. Delays in the diagnosis of pneumothorax can lead to severe complications and even mortality. A significant challenge in pneumothorax diagnosis is the shortage of radiologists, resulting in the absence of written reports in plain X-rays and, consequently, impacting patient care. In this paper, we propose an automatic triage system for pneumothorax detection in X-ray images based on deep learning. We address this problem from the perspective of multi-source domain adaptation where different datasets available on the Internet are used for training and testing. In particular, we use datasets which contain chest X-ray images corresponding to different conditions (including pneumothorax). A convolutional neural network (CNN) with an EfficientNet architecture is trained and optimized to identify radiographic signs of pneumothorax using those public datasets. We present the results using cross-dataset validation, demonstrating the robustness and generalization capabilities of our multi-source solution across different datasets. The experimental results demonstrate the model’s potential to assist clinicians in prioritizing and correctly detecting urgent cases of pneumothorax using different integrated deployment strategies. Full article

Background/Objectives: Additive manufacturing (AM) enables the production of cementless acetabular cups with porous surfaces that facilitate early osseointegration. Directed energy deposition (DED), a form of AM, allows the direct welding of porous structures onto metal substrates without requiring a vacuum environment, offering advantages over conventional powder bed fusion methods. Despite growing interest in DED, no prospective clinical studies evaluating DED-based acetabular components have been published to date. This study assessed short-term outcomes of a DED-based 3D-printed acetabular cup in total hip arthroplasty (THA). Methods: A total of 120 patients who underwent primary cementless THA using the Corentec Mirabo Z^® acetabular cup were prospectively enrolled. Among them, 124 hips from 100 patients who had completed a minimum of 24 months of follow-up were included in the analysis. Clinical outcomes were assessed using the Harris hip score (HHS), WOMAC, EQ-5D-5L, and pain NRS. Radiographic evaluation included measurements of cup position, osseointegration, and detection of interfacial or polar gaps on CT and plain radiographs. Implant-related complications were also recorded. Results: At a mean follow-up of 34.6 months, the implant survival rate was 99.3%, with one revision due to suspected osseointegration failure. The HHS improved from 56.6 to 91.4 at 24 months, and the NRS decreased from 6.2 to 1.1 (both p < 0.001). Interfacial gaps were observed in 58.1% of cases on CT, though most were <1 mm and not clinically significant. Common postoperative issues included greater trochanteric pain syndrome, squeaking, and iliotibial band tightness, all of which were resolved with conservative treatment. Conclusions: DED-based 3D-printed acetabular cups demonstrated favorable short-term clinical and radiographic outcomes, with high survivorship and reliable early osseointegration in cementless THA. Full article

Background/Objectives: Akin osteotomy, in the context of corrective surgery for hallux valgus, is an effective tool available to surgeons. However, few studies have thoroughly investigated the anatomical and technical characteristics to be considered in order to perform an optimal osteotomy. This cross-sectional observational study aims to identify the ideal site for performing Akin osteotomy and to identify the factors that influence its corrective power. Methods: To this end, an analysis was conducted on a random sample of 100 patients (186 feet) who underwent X-rays without surgical treatment. Variations in the width between the metaphysis and diaphysis were measured at five different points. For each cut level, corresponding to wedge bases of 2, 3 and 4 mm, three corrective angles were calculated. In addition, the distance between the cut line and the joint was recorded. Results: The base width ranged from 12.6 to 23.2 mm, showing greater variability in the metaphyseal region. The corrective power of the osteotomy showed wide variability, ranging from 5.9 to 18.4 degrees. Four determining factors emerged: the width of the base, the inclination of the medial cortex, the height at which the cut is made and the thickness of the wedge of bone removed. The data obtained suggest that osteotomy should not be performed less than 10 mm from the joint line to avoid the risk of joint invasion. Conclusions: In conclusion, there is no universally ideal site for performing an Akin osteotomy: the choice depends on the degree of correction desired, which in turn is influenced by the factors identified in the study. Full article

Background and clinical significance: Osteonecrosis of the femoral head (ONFH) is a disease of the epiphysis caused by the death of osteocytes and osteoblasts, resulting in debilitating pain. ONFH can be traumatic or nontraumatic, with prolonged glucocorticoid use being the leading cause of nontraumatic ONFH. Atopic dermatitis (AD) is a chronic inflammatory skin condition typically treated with topical corticosteroids. ONFH following topical corticosteroid treatment is exceedingly rare, with limited documentation in the literature. We present a case of an under-recognized complication of prolonged topical corticosteroid treatment. Case presentation: We report a case of a 29-year-old Caucasian male patient with sharp right hip pain. Plain radiographs, a CT scan, and an MRI indicated Ficat and Arlet stage 3 ONFH. The patient reported the prolonged uncontrolled use of topical mometasone furoate for five years due to AD. Following the diagnosis, topical corticosteroids were discontinued, and the treatment was shifted to tacrolimus and, subsequently, to oral methotrexate with folic acid. The patient underwent a total hip arthroplasty in June 2022. Given his young age and poor response to previous treatments, he was transitioned to upadacitinib, which led to significant improvement without skin flare-ups or postoperative hip pain. Conclusions: This case highlights the rare, but serious, risk of ONFH associated with long-term topical corticosteroid use. It underscores the importance of monitoring systemic side effects in dermatological therapies and educating patients on proper corticosteroid use. Alternative treatments, such as upadacitinib, should be considered in young male patients to prevent severe complications. Full article

Minimal facet and lateral mass fractures of the subaxial cervical spine (C3–C7) are a distinct subset of spinal injuries that present diagnostic and therapeutic challenges. These fractures often result from low-energy trauma or hyperextension mechanisms. They are frequently stable. However, subtle fracture instability and associated soft tissue injuries may lead to delayed instability, neurological compromise, and/or chronic severe pain if not properly identified. Accurate diagnosis relies on a combination of plain radiography, high-resolution computed tomography (CT), and magnetic resonance imaging (MRI) to assess bony and ligamentous integrity. Treatment strategy is determined based on fracture stability, neurological status, and radiographic findings. Most stable fractures can be effectively treated with conservative treatment, allowing for natural healing while minimizing complications. However, when instability is suspected—such as those with significant disc and ligamentous injuries, progressive deformity, or neurological deficits—surgical stabilization may be considered. The presence of vertebral artery injury (VAI) can further complicate management. To mitigate the risk of stroke, a multidisciplinary approach that includes neurosurgery, vascular surgery, and interventional radiology is needed. Surgical treatment aims to restore spinal alignment, maintain stability, and prevent further neurological deterioration with approaches tailored to individual fracture patterns and patient-specific factors. Advances in surgical techniques, perioperative management, and endovascular interventions for VAI continue refining treatment options to improve clinical outcomes while minimizing complications. Despite increasing knowledge of these fractures and associated vascular injuries, optimal treatment strategies remain unclear due to limited high-quality evidence. This review provides a comprehensive analysis of the anatomy, biomechanics, classification, imaging modalities, and treatment strategies for minimal facet and lateral mass fractures in the subaxial cervical spine, highlighting recent advancements in diagnostic tools, therapeutic approaches, and managing vertebral artery injuries. A more precise understanding of the natural history and optimal management of these injuries will help spine specialists refine clinical decision-making and improve patient outcomes. Full article

Background and Objectives: To investigate the natural metatasophalangeal (MTP) joint distance, we studied the appropriate degree of distraction for arthroscopy and the associated factors, including age, gender, and body mass index (BMI). Materials and Methods: Sixty-seven patients who underwent MTP joint arthroscopy or foot and ankle surgery from April 2013 to June 2020 were enrolled. Foot plain radiographs were taken using a mini-fluoroscan with no traction, manual traction, and traction of 5 pounds, 10 pounds, and 15 pounds to measure the MTP joint distance. Age, gender, and BMI were compared as associated factors. The minimum joint distance of MTP joint arthroscopy was defined as 2.8 mm, which was the sheath size of a 1.9 mm, 30° high-definition arthroscope. Results: Regarding natural MTP joint space sizes, the MTP-2 joint had the largest joint size (2.39 ± 0.37 mm). The MTP-5 joint had the smallest joint size (1.59 ± 0.34 mm). Traction of 10 lb was an appropriate distraction force for the MTP-1 joint (3.09 ± 0.03 mm) and MTP-4 joint (3.07± 0.47 mm) in arthroscopy. Traction of 5lb was an appropriate distraction force for the MTP-2 (3.32 ± 0.60 mm), MTP-3 (2.89 ± 0.50 mm), and MTP-5 (2.97 ± 0.49 mm) joints. For the MTP-1 and MTP-4 joints, males had significantly greater joint space sizes than females for no traction (p = 0.039), manual traction (p = 0.002), and traction of 5 pounds (p = 0.004), 10 pounds, (p = 0.013), and 15 pounds (p = 0.024). There was no statistically significant difference in joint space size according to age or BMI for any MTP joints (p > 0.05). Conclusions: Among natural joint spaces without traction, the MTP-2 joint had the largest joint size while the MTP-5 joint had the smallest joint size. In MTP joint arthroscopy, a traction power of 10 lb is sufficient for appropriate distraction of all MTP joints. Less distraction power is required for males than for females, especially for the MTP-1 and MTP-4 joints. Full article

Background: The accuracy of fracture diagnosis through radiographic imaging largely depends on image quality and the interpreter’s experience. In resource-limited settings (minimal-resource settings), imaging quality is often lower than in standard-resource facilities, potentially affecting diagnostic accuracy. Objective: This study aims to compare the diagnostic accuracy of plain radiograph interpretations between minimal-resource and standard-resource methods and assess the influence of interpreter experience on diagnostic precision. Methods: This cross-sectional study is based on secondary data from patients’ medical records at the Dr. Zainoel Abidin General Hospital (RSUDZA) Banda Aceh, Indonesia. Comparisons between minimal-resource and standard-resource interpretations were made and validated using a reference standard (gold standard). Statistical analyses included diagnostic testing, Chi-square tests, and ROC curve analysis to evaluate sensitivity, specificity, and accuracy. Results: The findings indicate that standard-resource radiographs have significantly higher accuracy than minimal-resource radiographs (p < 0.05). Radiologists demonstrated the highest diagnostic accuracy compared to general practitioners and radiology residents. Conclusions: The standard-resource method is superior in detecting fractures compared to the minimal-resource method. Enhancing imaging quality and providing additional training for medical personnel are essential to improve diagnostic accuracy in resource-limited settings. Full article

Background/Objectives: This study aims to develop and validate a YOLOv3-based deep learning model for detecting ossification of the posterior longitudinal ligament (OPLL) and ossification of the ligamentum flavum (OLF) on lateral thoracic radiographs, improving early diagnosis and screening accessibility. Methods: A retrospective dataset of 356 lateral thoracic radiographs, including 176 with OPLL or OLF and 180 controls, was annotated by spine surgeons. The YOLOv3 model was trained using data augmentation and evaluated via five-fold cross-validation, with accuracy, precision, recall, and F1-score compared to two spine surgeons. Results: The model achieved 80.6% accuracy, 70.3% precision, 92.6% recall, and 79.9% F1-score, surpassing spine surgeons in accuracy and recall, especially for combined OPLL and OLF cases. Detection accuracy was 81.1% for OPLL, 53.3% for OLF, and 86.3% for combined cases. Conclusions: The YOLOv3-based model provides high accuracy and robust detection of OPLL and OLF on plain radiographs, offering an efficient and accessible screening tool. Full article

Background: Recent studies indicate that the organo-axial subtype of a sigmoid volvulus is more prevalent than the conventional mesentero-axial subtype. Our study aimed to assess the clinical and radiological findings that differentiate between these two subtypes, as well as to ascertain treatment outcomes and prognostic characteristics. Methods: A retrospective review included 54 patients, during which abdominal plain radiographs and computed tomography images were analyzed by two radiologists, and data on recurrence, mortality, and treatment outcomes were documented. Results: The mesentero-axial subtype comprised 40 cases (74%). No distinct radiographic findings were observed to differentiate between the two groups. In computed tomography, the sole significant parameter for differentiation was the number of transition zones. The diameter of the segment exhibiting a volvulus was greater in instances of the mesentero-axial subtype. The endoscopic detorsion treatment proved ineffective in five patients within the mesentero-axial sigmoid volvulus cohort. Conclusions: Identifying these two types of SV on CT images is essential because of their distinct prognoses and therapeutic results. Full article

Charcot Neuro-Osteoarthropathy (CNO) is a debilitating complication predominantly affecting individuals with diabetes and peripheral neuropathy. Radiological assessment plays a central role in the diagnosis, staging, and management of CNO. While plain radiographs remain the cornerstone of initial imaging, advanced modalities such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) have significantly enhanced diagnostic accuracy. Nuclear imaging, including bone scintigraphy, radiolabeled leukocyte scans, and FDG-PET/CT, offers additional diagnostic precision in complex cases, especially when differentiating CNO from infections or evaluating patients with metal implants. This review underscores the importance of a multimodal imaging approach suited to the clinical stage and specific diagnostic challenges of CNO. It highlights the critical need for standardized imaging protocols and integrated diagnostic algorithms that combine radiological, clinical, and laboratory findings. Advances in imaging biomarkers and novel techniques such as diffusion-weighted MRI hold promise for improving early detection and monitoring treatment efficacy. In conclusion, the effective management of CNO in diabetic foot patients requires a multidisciplinary approach that integrates advanced imaging technologies with clinical expertise. Timely and accurate diagnosis not only prevents debilitating complications but also facilitates the development of personalized therapeutic strategies, ultimately improving patient outcomes. Full article

Background/Objectives: Plain radiography is the primary diagnostic tool for calcific tendinitis of the shoulder. Several qualitative grading methods have been proposed to represent the pathophysiologic phase and guide treatment decisions. However, these methods have demonstrated low reliability, complicating their effectiveness for such purposes. This study aims to perform the first quantitative analysis of calcific lesions using radiographic imaging and explore their correlation with ultrasonographic parameters to enhance their diagnostic utility. Methods: A total of 57 shoulders presenting with painful calcific tendinitis in either the supraspinatus or subscapularis tendon were reviewed. The calcific deposits and tendon regions of interest were meticulously identified and annotated. Image brightness was reduced to 256 grayscale levels, and descriptive and heterogeneity parameters, including skewness, kurtosis, complexity, and entropy, were quantified and analyzed. Results: In the region of calcification, the average grayscale values were 21.69 units higher than those of tendon tissue. All spatial heterogeneity parameters, except for skewness, demonstrated statistically significant differences when compared with the adjacent tendon. Notably, entropy and complexity were the most distinctive features, with an area under the curve of 0.93 and cut-off values of 4.62 and 4.18, respectively. Significant correlations were observed between the heterogeneity parameters and ultrasonographic findings, such as bursal contact and peri-calcific hypoattenuation. Conclusions: Calcific deposits demonstrated not only increased brightness in grayscale levels but also distinct spatial heterogeneity. The correlation with ultrasonographic findings indicates that these heterogeneity parameters may reflect underlying pathophysiological characteristics. Future prospective research could explore the whole temporal changes of calcifications more thoroughly. Full article

Background/Objectives: Developing high-performance artificial intelligence (AI) models for rare diseases is challenging owing to limited data availability. This study aimed to evaluate whether a novel three-class annotation method for preparing training data could enhance AI model performance in detecting osteosarcoma on plain radiographs compared to conventional single-class annotation. Methods: We developed two annotation methods for the same dataset of 468 osteosarcoma X-rays and 378 normal radiographs: a conventional single-class annotation (1C model) and a novel three-class annotation method (3C model) that separately labeled intramedullary, cortical, and extramedullary tumor components. Both models used identical U-Net-based architectures, differing only in their annotation approaches. Performance was evaluated using an independent validation dataset. Results: Although both models achieved high diagnostic accuracy (AUC: 0.99 vs. 0.98), the 3C model demonstrated superior operational characteristics. At a standardized cutoff value of 0.2, the 3C model maintained balanced performance (sensitivity: 93.28%, specificity: 92.21%), whereas the 1C model showed compromised specificity (83.58%) despite high sensitivity (98.88%). Notably, at the 25th percentile threshold, both models showed identical false-negative rates despite significantly different cutoff values (3C: 0.661 vs. 1C: 0.985), indicating the ability of the 3C model to maintain diagnostic accuracy at substantially lower thresholds. Conclusions: This study demonstrated that anatomically informed three-class annotation can enhance AI model performance for rare disease detection without requiring additional training data. The improved stability at lower thresholds suggests that thoughtful annotation strategies can optimize the AI model training, particularly in contexts where training data are limited. Full article

Objective: This study evaluated the effect of three-dimensional (3D) volumetric humeral canal fill ratios (VFR) of reverse shoulder arthroplasty (RSA) short and standard stems on biomechanical stability and bone deformations in the proximal humerus. Methods: Forty cadaveric shoulder specimens were analyzed in a clinical computed tomography (CT) scanner allowing for segmentation of the humeral canal to calculate volumetric measures which were verified postoperatively with plain radiographs. Virtual implant positioning allowed for group assignment (VFR < 0.72): Standard stem with low (n = 10) and high (n = 10) filling ratios, a short stem with low (n = 10) and high filling ratios (n = 10). Biomechanical testing included cyclic loading of the native bone and the implanted humeral component. Optical recording allowed for spatial implant tracking and the quantification of cortical bone deformations in the proximal humerus. Results: Planned filling ratios based on 3D volumetric measures had a good-to-excellent correlation (ICC = 0.835; p < 0.001) with implanted filling ratios. Lower canal fill ratios resulted in significantly higher variability between short and standard stems regarding implant tilt (820 N: p = 0.030) and subsidence (220 N: p = 0.046, 520 N: p = 0.007 and 820 N: p = 0.005). Higher filling ratios resulted in significantly lower bone deformations in the medial calcar area compared to the native bone, while the bone deformations in lower filling ratios did not differ significantly (p > 0.177). Conclusions: Lower canal filling ratios maintain dynamic bone loading in the medial calcar of the humerus similar to the native situation in this biomechanical loading setup. Short stems implanted with a low filling ratio have an increased risk for implant tilt and subsidence compared to high filling ratios or standard stems. Full article

Background: Knee osteoarthritis (OA) is a prevalent degenerative joint disease significantly impacting global health. Early and accurate diagnosis is crucial for effective management, but traditional methods often rely on subjective assessments. This study evaluates the efficacy of a deep learning model implemented through a no-code AI platform for diagnosing and grading knee OA from plain radiographs. Methods: We utilized the Osteoarthritis Initiative (OAI) dataset, comprising knee X-ray data from 1526 patients. The data were split into training (47.0%), validation (26.5%), and test (26.5%) sets. We employed a ResNet101 model on the DEEP:PHI no-code AI platform for image analysis. The model was trained to classify knee OA into five grades (0–4) based on the Kellgren–Lawrence scale. Results: Our AI model demonstrated high accuracy in distinguishing between different OA grades, with particular strength in early-stage detection. The model achieved optimal performance at 20 epochs, suggesting efficient learning dynamics. Grad-CAM visualizations were used to enhance the interpretability of the model’s decision-making process. Conclusions: This study demonstrates the potential of AI, implemented through a no-code platform, to accurately diagnose and grade knee OA from radiographs. The use of a no-code AI platform such as DEEP:PHI represents a step towards democratizing AI in healthcare, enabling the rapid development and deployment of sophisticated medical AI applications without extensive coding expertise. This approach could significantly enhance the early detection and management of knee OA, potentially improving patient outcomes and streamlining clinical workflows. Full article