CADe & CAPs technology based on artificial intelligence algorithms can assist endoscopists in detecting colorectal neoplasia and sizing abnormalities. Argus is vendor-neutral technology by integrating with existing ERWs/EHRs, scopes and processors to create the least amount of workflow changes for clinicians. Argus is launched during the procedure to assist with the detection and sizing of abnormalities utilizing artificial intelligence and machine learning. Argus simultaneously captures the highest quality images and video from the processor to aid in the decision making using CADe (computer-aided detection) and CAPs (computer-aided sizing) for the clinician to determine a treatment plan and generate reports. Utilizing Argus during a colonoscopy can increase Adenoma Detection Rates (ADRs) and improve patient recall times.

The addition of real-time computer-aided detection in colonoscopy significantly increased the adenoma detection rate and adenomas detected per colonoscopy in colorectal cancer screening, according to a presentation at ESGE Days. "[An] expert endoscopist can benefit from the artificial intelligence, increasing the detection," Alessandro Repici, MD, professor of gastroenterology, director of digestive endoscopy unit at Humanitas Research Hospital in Rozzano, Italy, said during his presentation. "This benefit is prevalent when adhered by the expert endoscopist including an interference between the [computer-aided detection (CAD-e)] and the level of experience of the operator." At five European centers, Repici and colleagues assessed 660 patients who underwent screening colonoscopies for CRC, post-polypectomy surveillance, or workup due to positive results from a fecal immunochemical test or signs or symptoms of CRC. Investigators randomly assigned patients to either high-definition colonoscopies with real-time computer-aided detection (CAD-e group) or without (control group). A minimum withdrawal time of 6 minutes was needed. Adenoma detection rate served as the primary outcome. Other outcomes included adenomas detected per colonoscopy and withdrawal time. The adenoma detection rate in the CAD-e group (53.3%) was significantly higher compared with the control group (44.2%;

Breast cancer is the most common cancer and is the leading cause of cancer death among women worldwide. Detection of breast cancer, while it is still small and confined to the breast, provides the best chance of effective treatment. Computer Aided Detection (CAD) systems that detect cancer from mammograms will help in reducing the human errors that lead to missing breast carcinoma. Literature is rich of scientific papers for methods of CAD design, yet with no complete system architecture to deploy those methods. On the other hand, commercial CADs are developed and deployed only to vendors' mammography machines with no availability to public access. This paper presents a complete CAD; it is complete since it combines, on a hand, the rigor of algorithm design and assessment (method), and, on the other hand, the implementation and deployment of a system architecture for public accessibility (system). (1) We develop a novel algorithm for image enhancement so that mammograms acquired from any digital mammography machine look qualitatively of the same clarity to radiologists' inspection; and is quantitatively standardized for the detection algorithms. (2) We develop novel algorithms for masses and microcalcifications detection with accuracy superior to both literature results and the majority of approved commercial systems. (3) We design, implement, and deploy a system architecture that is computationally effective to allow for deploying these algorithms to cloud for public access.

Motivated by the problem of computer-aided detection (CAD) of pulmonary nodules, we introduce methods to propagate and fuse uncertainty information in a multi-stage Bayesian convolutional neural network (CNN) architecture. The question we seek to answer is "can we take advantage of the model uncertainty provided by one deep learning model to improve the performance of the subsequent deep learning models and ultimately of the overall performance in a multi-stage Bayesian deep learning architecture?". Our experiments show that propagating uncertainty through the pipeline enables us to improve the overall performance in terms of both final prediction accuracy and model confidence.

Deep learning, also known as artificial intelligence, will increasingly be used in the interpretation of medical images to address many long-standing industry challenges. This will lead to a $300 million market by 2021, according to a new report by Signify Research, an independent supplier of market intelligence and consultancy to the global healthcare information technology industry. In most countries, there are not enough radiologists to meet the ever-increasing demand for medical imaging. Consequently, many radiologists are working at full capacity. The situation will likely get worse, as imaging volumes are increasing at a faster rate than new radiologists entering the field.