Case Report A 15-year-old male was referred by his general practitioner for evaluation of non-traumatic, painful lump on the medial aspect of his right tibia. The patient was known with neurofibromatosis type 1 (NF-1). Radiographs (Figure 1) demonstrated a soft tissue swelling on the medial side of the tibial shaft outlined by a thin calcified rim (arrowheads). A smaller, similar lesion could be seen more cranially (arrow). Ultrasound (US) (Figure 2A-C) showed a subperiosteal collection (circle) with an internal fluid-fluid level (arrow) as well as calcification of the surrounding periosteum. The calcified periosteum is surrounded by a heterogeneous, vascularized soft tissue cuff (arrowheads). MRI (Figure 3A-D) demonstrated a subperiosteal collection with signal properties of a chronic hematoma (triangle): T1/T2-isointense in the periphery of the lesion but T1-iso/T2-hyperintense in the centre (Figure 3C and 3A, respectively, for T1-and T2-weighted imaging). Moreover, there was the additional finding of a T2-hyperintense, T1-isointense, fat-poor (Figure 3B), enhancing tissue (star in Figure 3D), located medially and posteriorly with respect to the hematoma, corresponding to the described soft tissue cuff on US. These MRI features were suggestive of a plexiform neurofibroma of the saphenous nerve, that has caused an ossifying subperiosteal hematoma due to chronic erosion of the tibial cortex. Note the smooth cortical thinning on MRI (dashed arrow), the mass effect exerted on the tibialis posterior and flexor digitorum muscles as well as the surrounding soft tissue oedema. After three weeks, the swelling and pain had decreased significantly and it was decided not to perform any more imaging.

Background: Conventional radiography faces high error rates (3–10%) due to heavy clinical workloads. While AI has emerged as a supportive tool, there is an evidence gap regarding the clinical utility of integrated AI systems in detecting both skeletal and thoracic abnormalities. Objectives: This large-scale, international multicenter study aims to validate the performance of a unified radiographic AI suite across an expanded diagnostic scope while confirming its continued robustness. Methods: A retrospective performance evaluation was conducted using 21,581 adult and pediatric X-rays collected from 20 countries. The reference standard was established through independent review by two expert readers, with adjudication of a third radiologist in cases of discordance. Diagnostic metrics, including Area Under the Curve (AUC), sensitivity, and specificity, were calculated for all 18 pathologies. Subgroup analysis was performed by patients’ age, sex, and country of acquisition. Results: For the nine findings within the expanded scope, AUC values exceeded 96.1%, with sensitivity and specificity ranges from 94.5 to 98.8% and 86.6 to 96.1%, respectively. Similarly, for the nine historically validated findings, AUCs remained above 96.1%, with sensitivity and specificity localized between 94.5 and 97.8% and 84.6 and 89.4%, respectively. Consistency was maintained across subgroups. Conclusions: The results confirm the potential of deep learning to transition from narrow, task-specific tools to a unified, high-performance diagnostic system.