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Background: This study aimed to evaluate the impact of an AI-assisted fracture detection program on radiology residents' performance in pediatric and adult trauma patients and assess its implications for residency training. Methods: This study, conducted retrospectively, included 200 radiographs from participants aged 1 to 95 years (mean age: 40.7 ± 24.5 years), encompassing various body regions. Among these, 50% (100/200) displayed at least one fracture, totaling one hundred thirty-five fractures, assessed by four radiology residents with different experience levels. A machine learning algorithm was employed for fracture detection, and the ground truth was established by consensus among two experienced senior radiologists. Fracture detection accuracy, reporting time, and confidence were evaluated with and without AI support. Results: Radiology residents' sensitivity for fracture detection improved significantly with AI support (58% without AI vs. 77% with AI, p < 0.001), while specificity showed minor improvements (77% without AI vs. 79% with AI, p = 0.0653). AI stand-alone performance achieved a sensitivity of 93% with a specificity of 77%. AI support for fracture detection significantly reduced interpretation time for radiology residents by an average of approximately 2.6 s (p = 0.0156) and increased resident confidence in the findings (p = 0.0013). Conclusion: AI support significantly enhanced fracture detection sensitivity among radiology residents, particularly benefiting less experienced radiologists. It does not compromise specificity and reduces interpretation time, contributing to improved efficiency. This study underscores AI's potential in radiology, emphasizing its role in training and interpretation improvement.
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BACKGROUND: Solitary fibrous tumours are rare. The aim of this study is to describe the clinical features, therapy and outcome of affected patients and to identify factors associated with recurrence. METHODS: Retrospective study of a cohort of 20 patients who underwent surgery for orbital solitary fibrous tumour at the University Department of Oral and Maxillofacial Surgery between 2002 and 2023. Demographic, clinical, and therapeutic data as well as tumour follow-up results were collected. Tumour volume and molecular genetic mutations were retrospectively determined. RESULTS: The median patient age was 49.5 years at initial surgery. The left orbit was affected in 65% of cases. The most common clinical symptom was proptosis (80%). This was reported with a mean lateral difference of 3.9 mm (range: 1â-â10 mm). The tumours were localised predominantly in the intra- and extraconal space, craniolateral quadrant and middle third. The median tumour volume was 7.66 cm³ (range 2.15â-â12.57 cm³). In all patients, the diagnosis was made by pathological examination. All tumours investigated showed a NAB2-STAT6 mutation. The most frequently detected mutation was the fusion NAB2 exon 4 - STAT6 exon 2. All patients were initially managed with frontolateral orbitotomy. Incomplete resection (R1-status) occurred in 35% (n = 7). The recurrence rate was 25% (n = 5), with a median disease-free interval of 45.5 months (range 23â-â130). 80% (n = 4) of recurrences were initially R1-resected. CONCLUSION: Orbital solitary fibrous tumours are rare tumours and are clinically manifested by signs of displacement of orbital structures. Diagnosis is made by histology and immunohistochemistry and can be proven with the molecular genetic detection of the NAB2-STAT6 mutation. The therapy of choice is complete surgical resection. R1-resection is more likely in the intraconal location as well as in location in the posterior third of the orbit - due to difficult surgical accessibility. The greatest risk factor for the development of recurrence is incomplete surgical excision. Late recurrences are possible, which is why a long-term connection to a specialised clinic is necessary.
