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.
Urology fellow, Jeremy Fallot, and nurse, Shauna Harnedy, assist in robotic surgery by Ruban Thanigasalam (out of view) in Sydney, Australia.Credit: Ken Leanfore for Nature Loved by surgeons and patients alike for its ease of use and faster recovery times, the da Vinci surgical robot is less invasive than conventional procedures, and lacks the awkwardness of laparoscopic (keyhole) surgery. But the robot's US$2-million price tag and negligible effect on cancer outcomes is sparking concern that it's crowding out more affordable treatments. There are more than 5,500 da Vinci robots globally, manufactured by California-based tech giant, Intuitive. The system is used in a range of surgical procedures, but its biggest impact has been in urology, where it has a market monopoly on robot-assisted radical prostatectomies (RARP), the removal of the prostate and surrounding tissues to treat localized cancer. Uptake in the United States, Europe, Australia, China and Japan for performing this procedure has been rapid.
New artificial intelligence (AI) capabilities may make it possible to improve the effectiveness of brachytherapy for men with prostate cancer (PCa) by almost instantly generating dosage plans, according to investigators. In a typical high-dose rate (HDR) brachytherapy procedure for PCa, needle applicators are first inserted by the physician to the tumor target. A planner then develops a treatment plan manually. During this time the patient carries the needles, waiting for the planning to finish. With the current standard of care, it takes up to an hour or more to generate a high-quality plan.
New Delhi: Advances in technology and medical research could mean seismic changes in the healthcare industry. Soon, cancer, heart disease, diabetes and other debilitating illnesses could be defeated - perhaps, 20 years from now (unbelievable as it may sound) - thanks to scientists, medical doctors and researchers who are working vigorously, making stupendous progress on all these fronts. Over the last decade, healthcare is one of the industries that has evolved the most, yet, we're going to see changes in the way diseases are being treated. It's evident that we're going to witness drastic changes in a number of dimensions - from robots in the role of healthcare professionals to smart technology and artificial intelligence tools that will improve the quality of care and population health. Dr Sanjay Pandey, Head - Andrology and Reconstructive Urology - Kokilaben Dhirubhai Ambani Hospital, Mumbai, spoke on how digital technology, robotics and AI are transforming the face of medicine.
"Our results show that it is possible to train an AI system to detect and grade prostate cancer on the same level as leading experts," study author Martin Eklund, with Karolinska Institutet in Sweden, said in a statement. "This has the potential to significantly reduce the workload of uro-pathologists and allow them to focus on the most difficult cases."
LONDON: Scientists have developed an artificial intelligence (AI) based method that is as good at identifying and grading prostate cancer as world-leading uro-pathologists. The AI-system has the potential to solve one of the bottlenecks in today's prostate cancer histopathology by providing more accurate diagnosis and better treatment decisions, according to the study published in The Lancet Oncology journal. "Our results show that it is possible to train an AI-system to detect and grade prostate cancer on the same level as leading experts," said Martin Eklund, an associate professor at Karolinska Institutet in Sweden. "This has the potential to significantly reduce the workload of uro-pathologists and allow them to focus on the most difficult cases," Eklund said. A problem in today's prostate pathology is that there is a certain degree of subjectivity in the assessments of the biopsies, researchers said.
We explored metabolic pathways related to early-stage BCa (Galactose metabolism and Starch and sucrose metabolism) and to late-stage BCa (Glycine, serine, and threonine metabolism, Arginine and proline metabolism, Glycerophospholipid metabolism, and Galactose metabolism) as well as those common to both stages pathways. The central metabolite impacting the most cancerogenic genes (AKT, EGFR, MAPK3) in early stage is d-glucose, while late-stage BCa is characterized by significant fold changes in several metabolites: glycerol, choline, 13(S)-hydroxyoctadecadienoic acid, 2′-fucosyllactose. Insulin was also seen to play an important role in late stages of BCa. The best performing model was able to predict metabolite class with an accuracy of 82.54% and the area under precision-recall curve (PRC) of 0.84 on the training set. The same model was applied to three separate sets of metabolites obtained from public sources, one set of the late-stage metabolites and two sets of the early-stage metabolites.
Recent years have witnessed a significant increase in the online sharing of medical information, with videos representing a large fraction of such online sources. Previous studies have however shown that more than half of the health-related videos on platforms such as YouTube contain misleading information and biases. Hence, it is crucial to build computational tools that can help evaluate the quality of these videos so that users can obtain accurate information to help inform their decisions. In this study, we focus on the automatic detection of misinformation in YouTube videos. We select prostate cancer videos as our entry point to tackle this problem. The contribution of this paper is twofold. First, we introduce a new dataset consisting of 250 videos related to prostate cancer manually annotated for misinformation. Second, we explore the use of linguistic, acoustic, and user engagement features for the development of classification models to identify misinformation. Using a series of ablation experiments, we show that we can build automatic models with accuracies of up to 74%, corresponding to a 76.5% precision and 73.2% recall for misinformative instances.
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.
Artificial intelligence (AI) – "the mimicking of human cognition by computers" – is a rapidly expanding field within medicine [1,2]. There is increasing evidence that AI may enhance the delivery of healthcare . A well-known example is an AI system known as'Watson' created by IBM which is a decision support tool towards the diagnosis and management of oncology patients at Memorial Sloan Kettering Cancer Center . As practitioners of medicine, we spend a lifetime refining our intuition with hard earned knowledge and skills to acquire the mindset for sophisticated decision-making in our increasingly complex patients. However, Watson can pull information and make decisions based on millions of such human physicians' life-time experiences, medical reports, patient records, clinical trials and published literature .