Collaborating Authors


AUA 2020: Automated Performance Metrics to Predict Continence Recovery After Robotic Radical Prostatectomy Utilizing Machine Learning


The robot-assisted radical prostatectomy was segmented into 12 steps, and for each step, 41 validated automated performance metrics were reported. The predictive models were trained with three data sets: 1) 492 automated performance metrics; 2) 16 clinicopathological data (for example prostate volume, Gleason score); 3) automated performance metrics plus clinicopathological data. The authors utilized a random forest model (800 trees) to predict continence recovery (no pads or one safety pad) at three and six months after surgery. The prediction accuracy was estimated through a 10-fold cross-validation process. The area under the curve (AUC) and standard error (SE) was used to estimate prediction accuracy. Finally, the out-of-bag Gini index was used to rank the variables of importance.

A new era: artificial intelligence and machine learning in prostate cancer


The current availability of ever-increasing computational power, highly developed pattern recognition algorithms and advanced image processing software working at very high speeds has led to the emergence of computer-based systems that are trained to perform complex tasks in bioinformatics, medical imaging and medical robotics. Accessibility to'big data' enables the'cognitive' computer to scan billions of bits of unstructured information, extract the relevant information and recognize complex patterns with increasing confidence. Computer-based decision-support systems based on machine learning (ML) have the potential to revolutionize medicine by performing complex tasks that are currently assigned to specialists to improve diagnostic accuracy, increase efficiency of throughputs, improve clinical workflow, decrease human resource costs and improve treatment choices. These characteristics could be especially helpful in the management of prostate cancer, with growing applications in diagnostic imaging, surgical interventions, skills training and assessment, digital pathology and genomics. Medicine must adapt to this changing world, and urologists, oncologists, radiologists and pathologists, as high-volume users of imaging and pathology, need to understand this burgeoning science and acknowledge that the development of highly accurate AI-based decision-support applications of ML will require collaboration between data scientists, computer researchers and engineers.

AI-based Healx raises $10 million for drug discovery - MedCity News


The researcher who invented Viagra and a colleague at Cambridge University have become the latest to join the ranks of drug developers using artificial intelligence and attracting attention from venture capitalists. Cambridge, UK-based Healx said Thursday that it had raised $10 million in a Series A funding round, led by London-based venture capital firm Balderton Capital. Fellow British venture capital firm Amadeus Capital Partners and Jonathan Milner – founder of life sciences supplier Abcam – also participated. Cambridge Rare Diseases Network founder Tim Guilliams and David Brown – who invented Pfizer's erectile dysfunction drug, which is now available as a generic – are the founders of Healx. The company uses the HealNet database, which maps more than 1 billion disease, patient and drug interactions and was built and maintained using machine learning techniques.

Touching personal medical stories are no substitute for science

New Scientist

MEDICAL screening is one of those issues where getting the scientific facts across is extremely challenging. Common sense suggests that routine screening must be a good thing: what harm could it do to systematically test everybody for diseases such as prostate and breast cancer? But as has been shown repeatedly, routine screening is often, on average, harmful. For every life saved through early diagnosis, many more are blighted by psychological trauma, invasive investigations or unnecessary treatments (see "How medicine got too good for its own good"). False negatives, meanwhile, can lead people who are actually ill to take no action.