Purpose Management of vestibular schwannoma (VS) is based on tumour size as observed on T1 MRI scans with contrast agent injection. The current clinical practice is to measure the diameter of the tumour in its largest dimension. It has been shown that volumetric measurement is more accurate and more reliable as a measure of VS size. The reference approach to achieve such volumetry is to manually segment the tumour, which is a time intensive task. We suggest that semi-automated segmentation may be a clinically applicable solution to this problem and that it could replace linear measurements as the clinical standard. Methods Using high-quality software available for academic purposes, we ran a comparative study of manual versus semiautomated segmentation of VS on MRI with 5 clinicians and scientists. We gathered both quantitative and qualitative data to compare the two approaches; including segmentation time, segmentation effort and segmentation accuracy. Results We found that the selected semi-automated segmentation approach is significantly faster (167 s vs 479 s, p < 0.001), less temporally and physically demanding and has approximately equal performance when compared with manual segmentation, with some improvements in accuracy. There were some limitations, including algorithmic unpredictability and error, which produced more frustration and increased mental effort in comparison with manual segmentation. Conclusion We suggest that semi-automated segmentation could be applied clinically for volumetric measurement of VS on MRI. In future, the generic software could be refined for use specifically for VS segmentation, thereby improving accuracy.

Objective Medical students, as clinicians and healthcare leaders of the future, are key stakeholders in the clinical roll-out of artificial intelligence-driven technologies. The authors aim to provide the first report on the state of artificial intelligence in medical education globally by exploring the perspectives of medical students. Methods The authors carried out a mixed-methods study of focus groups and surveys with 128 medical students from 48 countries. The study explored knowledge around artificial intelligence as well as what students wished to learn about artificial intelligence and how they wished to learn this. A combined qualitative and quantitative analysis was used. Results Support for incorporating teaching on artificial intelligence into core curricula was ubiquitous across the globe, but few students had received teaching on artificial intelligence. Students showed knowledge on the applications of artificial intelligence in clinical medicine as well as on artificial intelligence ethics. They were interested in learning about clinical applications, algorithm development, coding and algorithm appraisal. Hackathon-style projects and multidisciplinary education involving computer science students were suggested for incorporation into the curriculum. Conclusions Medical students from all countries should be provided teaching on artificial intelligence as part of their curriculum to develop skills and knowledge around artificial intelligence to ensure a patient-centred digital future in medicine. This teaching should focus on the applications of artificial intelligence in clinical medicine. Students should also be given the opportunity to be involved in algorithm development. Students in low- and middle-income countries require the foundational technology as well as robust teaching on artificial intelligence to ensure that they can drive innovation in their healthcare settings.

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OBJECTIVE Temporal lobe encephaloceles (TLENs) are a significant cause of medically refractory epilepsy, but there is little consensus regarding their workup and treatment. This study characterizes these lesions and their role in seizures and aims to standardize preoperative evaluation and surgical management. METHODS Patients with TLEN who had undergone resective epilepsy surgery from December 2015 to August 2020 at a single institution were included in the study. Medical records were reviewed for each patient to collect relevant seizure workup information including demographics, radiological findings, surgical data, and neuropsychological evaluation. RESULTS For patients who presented to the authors’ program with suspected medically intractable temporal lobe epilepsy (219 patients), TLEN was considered to be the epileptogenic focus in 5.5%. Ten patients with TLEN had undergone resection and were included in this study. Concordance between ictal scalp electroencephalography (EEG) lateralization and TLEN was found in 9/10 patients (90%), and 4/10 patients (40%) had signs suggestive of idiopathic intracranial hypertension (IIH). Surgical outcome was reported in patients with at least 12 months of follow-up (9/10). Patients with scalp EEG findings concordant with the TLEN side had a good outcome (Engel class I: 7 patients, class II: 1 patient). One patient with discordant EEG findings had a bad outcome (Engel class III). No significant neuropsychological deficits were observed after the surgery. CONCLUSIONS TLENs are epileptogenic lesions that should be screened for in patients with medically refractory epilepsy who have signs of IIH and no other lesions on MRI. Restricted resection is safe and effective in patients with scalp EEG findings concordant with TLEN.