ABSTRACT
OBJECTIVES: To compare diagnostic accuracy of a deep learning artificial intelligence (AI) for cervical spine (C-spine) fracture detection on CT to attending radiologists and assess which undetected fractures were injuries in need of stabilising therapy (IST). METHODS: This single-centre, retrospective diagnostic accuracy study included consecutive patients (age ≥18 years; 2007-2014) screened for C-spine fractures with CT. To validate ground truth, one radiologist and three neurosurgeons independently examined scans positive for fracture. Negative scans were followed up until 2022 through patient files and two radiologists reviewed negative scans that were flagged positive by AI. The neurosurgeons determined which fractures were ISTs. Diagnostic accuracy of AI and attending radiologists (index tests) were compared using McNemar. RESULTS: Of the 2368 scans (median age, 48, interquartile range 30-65; 1441 men) analysed, 221 (9.3%) scans contained C-spine fractures with 133 IST. AI detected 158/221 scans with fractures (sensitivity 71.5%, 95% CI 65.5-77.4%) and 2118/2147 scans without fractures (specificity 98.6%, 95% CI 98.2-99.1). In comparison, attending radiologists detected 195/221 scans with fractures (sensitivity 88.2%, 95% CI 84.0-92.5%, p < 0.001) and 2130/2147 scans without fracture (specificity 99.2%, 95% CI 98.8-99.6, p = 0.07). Of the fractures undetected by AI 30/63 were ISTs versus 4/26 for radiologists. AI detected 22/26 fractures undetected by the radiologists, including 3/4 undetected ISTs. CONCLUSION: Compared to attending radiologists, the artificial intelligence has a lower sensitivity and a higher miss rate of fractures in need of stabilising therapy; however, it detected most fractures undetected by the radiologists, including fractures in need of stabilising therapy. Clinical relevance statement The artificial intelligence algorithm missed more cervical spine fractures on CT than attending radiologists, but detected 84.6% of fractures undetected by radiologists, including fractures in need of stabilising therapy. KEY POINTS: The impact of artificial intelligence for cervical spine fracture detection on CT on fracture management is unknown. The algorithm detected less fractures than attending radiologists, but detected most fractures undetected by the radiologists including almost all in need of stabilising therapy. The artificial intelligence algorithm shows potential as a concurrent reader.
ABSTRACT
OBJECTIVE: Is sudden sensorineural hearing loss associated with Lyme disease in adults? STUDY DESIGN: A retrospective case report and a systematic literature search in PubMed and Embase were performed. RESULTS: We describe a patient presenting with sudden sensorineural hearing loss, followed by a facial paralysis, which could be attributed to Lyme disease confirmed by positive serology and a positive immunoblot. She was successfully treated with ceftriaxone, with recovery of the facial paralysis, although no recovery of the hearing loss was observed. A systematic literature search resulted in 4 relevant and valid articles revealing that confirmed positive serology for Borrelia burgdorferi varies from 0% to 21.3%, suggesting active Lyme disease as a cause in patients with sudden sensorineural hearing loss. Two studies demonstrated a significantly higher incidence of confirmed positive serology for Borrelia burgdorferi as compared with the incidence in the general local population. CONCLUSION: Literature suggests that sudden sensorineural hearing loss may coincide with Borrelia burgdorferi infection. A higher incidence of confirmed positive serology for Borrelia burgdorferi in patients with sudden deafness seems to be depending on the country and on the tests used to confirm Lyme disease. This should be taken into account if serologic testing for Lyme disease in patients with sudden deafness is considered.
