DAK-Gesundheit (Germany)

Fractures in children are common in emergency care, and accurate diagnosis is crucial to avoid complications affecting skeletal development. Limited access to pediatric radiology specialists emphasizes the potential of artificial intelligence (AI)-based diagnostic tools. This study evaluates the performance of the AI software BoneView® for detecting fractures of the upper extremity in children aged 2-18 years. A retrospective analysis was conducted using radiographic data from 826 pediatric patients presenting to the university's pediatric emergency department. Independent assessments by two experienced pediatric radiologists served as reference standard. The diagnostic accuracy of the AI tool compared to the reference standard was evaluated and performance parameters, e.g., sensitivity, specificity, positive and negative predictive values were calculated. The AI tool achieved an overall sensitivity of 89% and specificity of 91% for detecting fractures of the upper extremities. Significantly poorer performance compared to the reference standard was observed for the shoulder, elbow, hand, and fingers, while no significant difference was found for the wrist, clavicle, upper arm, and forearm. The software performed best for wrist fractures (sensitivity: 96%; specificity: 94%) and worst for elbow fractures (sensitivity: 87%; specificity: 65%). The software assessed provides diagnostic support in pediatric emergency radiology. While its overall performance is robust, limitations in specific anatomical regions underscore the need for further training of the underlying algorithms. The results suggest that AI can complement clinical expertise but should not replace radiological assessment. Question There is no comprehensive analysis of an AI-based tool for the diagnosis of pediatric fractures focusing on the upper extremities. Findings The AI-based software demonstrated solid overall diagnostic accuracy in the detection of upper limb fractures in children, with performance differing by anatomical region. Clinical relevance AI-based fracture detection can support pediatric emergency radiology, especially where expert interpretation is limited. However, further algorithm training is needed for certain anatomical regions and for detecting associated findings such as joint effusions to maximize clinical benefit.

Mold identification in clinical diagnostics is traditionally labor-intensive and is dependent on expert interpretation. MoldVision is a deep-learning approach that uses smartphone images of mold cultures to automate identification. We analyzed 161 clinical isolates across four common mold genera: Penicillium spp., Aspergillus spp. (with A. flavus and A. fumigatus), Fusarium spp., and Cladosporium spp. Daily images were captured from the top and bottom of culture plates over 5 days using a standardized smartphone setup, generating over 4,000 images. We trained three variations of Visual Geometry Group 16 (VGG16) convolutional neural networks (CNNs) and benchmarked the best-performing model (VGG16 with dual classification heads) against LightGBM models trained on pre-extracted features and human expert assessments at various time points. The best-performing VGG16 model achieved a mean (SD) AUROC of 92.7% ± 1.8% and sensitivity of 68.7% ± 2.6% across all species. Here, the performance in identifying Cladosporium spp. was best (AUROC of 99.9% ± 0.1%, fivefold cross-validation mean and SD). Regarding the evaluations over time, early-stage classification (days 1-2) was challenging (F1-score 38.8% ± 3.5% across all species) but improved significantly on days 3-5 (F1 92.1% ± 2.8% across all species). Compared to experts, MoldVision consistently showed superior performance, particularly in mature cultures, detecting subtle morphological features earlier and more accurately. Our results demonstrate that CNNs integrated with low-cost smartphone imaging can reliably classify mold species in routine diagnostics, outperforming human experts in many cases. This approach offers a practical and scalable solution for laboratories lacking specialized mycology expertise, especially in resource-limited settings.IMPORTANCETimely, accurate mold identification can save lives, yet today it often requires days of growth and scarce expert time. MoldVision offers a practical alternative: use a standard smartphone to photograph routine agar plates and let an AI model aid with species recognition. Trained on thousands of images across 5 days of growth, the system detects tell-tale colony patterns as they appear and, once growth is established (days 3-5), often matches or exceeds human readers. This could shorten reporting, prioritize which cultures need expert review, and extend reliable mycology support to laboratories without specialized equipment or staffing. Because it relies on affordable tools and a standardized imaging workflow, MoldVision is scalable to resource-limited settings. We also share data and code to encourage external validation and improvement, moving this concept toward faster and equitable fungal diagnostics. These findings highlight a practical path to amplify diagnostic capacity where it is needed most.