Use of deep learning for detection, characterisation and prediction of metastatic disease from computerised tomography: a systematic review.

CT is widely used for diagnosis, staging and management of cancer. The presence of metastasis has significant implications on treatment and prognosis. Deep learning (DL), a form of machine learning, where layers of programmed algorithms interpret and recognise patterns, may have a potential role in CT image analysis. This review aims to provide an overview on the use of DL in CT image analysis in the diagnostic evaluation of metastatic disease. A total of 29 studies were included which could be grouped together into three areas of research: the use of deep learning on the detection of metastatic disease from CT imaging, characterisation of lesions on CT into metastasis and prediction of the presence or development of metastasis based on the primary tumour. In conclusion, DL in CT image analysis could have a potential role in evaluating metastatic disease; however, prospective clinical trials investigating its clinical value are required.

Augmented reality in surgical training: a systematic review.

The aim of this systematic review is to provide an update on the current state of augmented reality (AR) in surgical training and to further report on any described benefits compared with traditional techniques. A PICO (Population, Intervention, Comparison, Outcome) strategy was adopted to formulate an appropriate research question and define strict search terms to be entered into MEDLINE, CENTRAL and Google Scholar. The search was returned on 12/09/2019. All returned results were screened first by title and then abstract. The systematic search returned a total of 236 results, of which 18 were selected for final inclusion. Studies covered the full range of surgical disciplines and reported on outcomes including operative duration, accuracy and postoperative complication rates. Due to the heterogeneity of the collected data, no meta-analysis was possible. Outcome measures of competency, surgical opinion and postoperative complication rate were in favour of AR technology while operative duration appears to increase.

'The House of God': reflections 40 years on, in conversation with author Samuel Shem.

The House of God is a seminal work of medical satire based on the gruelling internship experiences of Samuel Shem at the Beth Israel Hospital. Thirteen 'Laws' were offered to rationalise the seemingly chaotic patient management and flow. There have been large shifts in the healthcare landscape and practice since, so we consider whether these medical truisms are still applicable to contemporary National Health Service practice and propose updates where necessary:People are sometimes allowed to die.GOMERs (Get Out of My Emergency Room) still go to ground.Master yourself, join the multidisciplinary team.The patient is the one with the disease, but not the only one suffering.Placement (discharge planning) comes first.There is no body cavity that cannot be reached with a gentle arm and good interventional radiologists.Fit the rule to the patient rather than the patient to the rule.They can always pay you less.The only bad admission is a futile one.If you don't take a temperature you can't find a fever and if you are not going to act on it, don't do the test.Show me a BMS (best medical student) who ONLY triples my work, and I'll show you a future Foundation Year 1 doctor (FY1) who is an asset to the firm.Interpret radiology freely, but share your clinical findings with the radiologist and in a timely fashion.Doing nothing can be a viable option. These were developed in conversation with Samuel Shem, who also offers further insight on the creation of the original laws.