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Purpose: Non-contrast CT head scans provide rapid and accurate diagnosis of acute head injury; however, increased utilisation of CT head scans makes it difficult to prioritise acutely unwell patients and places pressure on busy emergency departments (EDs). This study validates an AI algorithm to triage patients presenting with Intracranial Haemorrhage (ICH) or Acute Infarct whilst also identifying a subset of patients as Normal, with the potential to function as a rule-out test. Methods: In total, 390 CT head scans were collected from 3 institutions in the UK, US and India. Ground-truth labels were assigned by 3 FRCR consultant radiologists. AI performance, as well as the performance of 3 independent radiologists, was measured against ground-truth labels. Results: The algorithm showed AUC values of 0.988 (0.978–0.994), 0.933 (0.901–0.961) and 0.939 (0.919–0.958) for ICH, Acute Infarct and Normal, respectively. Sensitivity/specificity for ICH and Acute Infarct were 0.988/0.925 and 0.833/0.927, respectively, compared to 0.907/0.991 and 0.618/0.977 for radiologists. AI rule-out of Normal scans achieved 0.93% negative predictive value (NPV) for the removal of 54.3% of Normal cases, compared to 86.8% NPV for radiologists. Conclusion: We show our algorithm can provide effective triage of ICH and Acute Infarct to prioritise acutely unwell patients. AI can also benefit clinical accuracy, with the algorithm identifying 91.3% of radiologist false negatives for ICH and 69.1% for Acute Infarct. Rule-out of Normal scans has huge potential for workload management in busy EDs, in this case removing 27.4% of all scans with no acute findings missed. [ABSTRACT FROM AUTHOR]

Aim: The aim of the study was o define the preoperative diagnostic clinical and radiological features in girls with congenital pouch colon (CPC). Materials and Methods: Records of 47 girls with CPC, managed from 1996 to 2018, were reviewed. There were two age groups: Group A (newborn to 12 months; n = 26) and Group B (>12 months to 20 years; n = 21). The important clinical and radiologic features to help in a preoperative diagnosis were noted. Results: The most common subtype was Type II (57.4%), followed by Type I (23.4%) and Type III (12.8%). The features common to both the groups were abdominal distension (A = 53.8%; B = 9.52%), severe perineal excoriation (A = 19.2%; B = 23/8%), and urinary incontinence (A = 30.7%; B = 85.7%). In addition, in Group B, fecaloma on abdominal palpation was noted in 28.6% of patients. The characteristic appearance of the perineum including external genitalia and findings on plain abdominal X-ray (AXR) were 100% accurate and hence diagnostic. These unique features helped us formulate an algorithm for preoperative diagnosis of this uncommon form of anorectal malformation in girls seen in North India. Conclusions: The characteristic features on clinical examination should alert one to the presence of CPC in the outpatient clinic. The AXR was diagnostic in 100% of cases and is mandatory. If any doubt persists, examination of the genitalia under anesthesia with more retraction of the labial folds and endoscopy can be performed for confirmation. These measures should enable a clinician to make an accurate preoperative diagnosis in every girl with CPC. [ABSTRACT FROM AUTHOR]