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Effective weight loss and reduction in comorbidities has been convincingly demonstrated with bariatric surgery. Concerns regarding increased perioperative complications and poor results have led to a reluctance to offer such surgery to older patients. We performed a systematic review and meta-analysis of the published evidence for those in the ≥55-year age group. An electronic search was conducted of MEDLINE, EMBASE, and the Cochrane Library databases from 1990 to December 2010. We included laparoscopic studies published in English where the results were broken down by surgical procedure, reporting a minimum 6-month follow-up for ≥10 patients aged ≥55. After an initial screen of 2,543 titles, 298 abstracts were reviewed. Eighteen studies were included in the analysis. Of these, 10 included patients undergoing laparoscopic Roux-en-Y gastric bypass (LRYGB) (663 patients), and 11 included patients undergoing laparoscopic adjustable gastric banding (LAGB) (543 patients). Meta-analyses of body mass index (BMI) reductions indicated sustained and clinically significant BMI reductions for both RYGB (mean percentage of excess weight loss at 1 year, 72.6 %) and LAGB (mean percentage of excess weight loss at 1 year, 39.1 %). The 30-day mortality was 0.30 and 0.18 % for LRYGB and LAGB, respectively. Meta-analysis of old versus young patients revealed better comorbidity and mortality outcomes for younger patients. Bariatric surgery for patients ≥55 years achieves weight loss and reduction in comorbidities and mortality comparable to the general bariatric surgery population. Based on the above findings, patients should not be denied bariatric surgery on the basis of age alone.

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Objectives The purpose of this study was to build a deep learning model to derive labels from neuroradiology reports and assign these to the corresponding examinations, overcoming a bottleneck to computer vision model development. Methods Reference-standard labels were generated by a team of neuroradiologists for model training and evaluation. Three thousand examinations were labelled for the presence or absence of any abnormality by manually scrutinising the corresponding radiology reports (‘reference-standard report labels’); a subset of these examinations (n = 250) were assigned ‘reference-standard image labels’ by interrogating the actual images. Separately, 2000 reports were labelled for the presence or absence of 7 specialised categories of abnormality (acute stroke, mass, atrophy, vascular abnormality, small vessel disease, white matter inflammation, encephalomalacia), with a subset of these examinations (n = 700) also assigned reference-standard image labels. A deep learning model was trained using labelled reports and validated in two ways: comparing predicted labels to (i) reference-standard report labels and (ii) reference-standard image labels. The area under the receiver operating characteristic curve (AUC-ROC) was used to quantify model performance. Accuracy, sensitivity, specificity, and F1 score were also calculated. Results Accurate classification (AUC-ROC > 0.95) was achieved for all categories when tested against reference-standard report labels. A drop in performance (ΔAUC-ROC > 0.02) was seen for three categories (atrophy, encephalomalacia, vascular) when tested against reference-standard image labels, highlighting discrepancies in the original reports. Once trained, the model assigned labels to 121,556 examinations in under 30 min. Conclusions Our model accurately classifies head MRI examinations, enabling automated dataset labelling for downstream computer vision applications. Key Points • Deep learning is poised to revolutionise image recognition tasks in radiology; however, a barrier to clinical adoption is the difficulty of obtaining large labelled datasets for model training. • We demonstrate a deep learning model which can derive labels from neuroradiology reports and assign these to the corresponding examinations at scale, facilitating the development of downstream computer vision models. • We rigorously tested our model by comparing labels predicted on the basis of neuroradiology reports with two sets of reference-standard labels: (1) labels derived by manually scrutinising each radiology report and (2) labels derived by interrogating the actual images.

BackgroundRobotically performed neurointerventional surgery has the potential to reduce occupational hazards to staff, perform intervention with greater precision, and could be a viable solution for teleoperated neurointerventional procedures.ObjectiveTo determine the indication, robotic systems used, efficacy, safety, and the degree of manual assistance required for robotically performed neurointervention.MethodsWe conducted a systematic review of the literature up to, and including, articles published on April 12, 2021. Medline, PubMed, Embase, and Cochrane register databases were searched using medical subject heading terms to identify reports of robotically performed neurointervention, including diagnostic cerebral angiography and carotid artery intervention.ResultsA total of 8 articles treating 81 patients were included. Only one case report used a robotic system for intracranial intervention, the remaining indications being cerebral angiography and carotid artery intervention. Only one study performed a comparison of robotic and manual procedures. Across all studies, the technical success rate was 96% and the clinical success rate was 100%. All cases required a degree of manual assistance. No studies had clearly defined patient selection criteria, reference standards, or index tests, preventing meaningful statistical analysis.ConclusionsGiven the clinical success, it is plausible that robotically performed neurointerventional procedures will eventually benefit patients and reduce occupational hazards for staff; however, there is no high-level efficacy and safety evidence to support this assertion. Limitations of current robotic systems and the challenges that must be overcome to realize the potential for remote teleoperated neurointervention require further investigation.

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