Recent studies have shown that artificial intelligence-based computer-aided detection systems possess great potential in reducing the heterogeneous performance of doctors during endoscopy. However, most existing studies are based on high-quality static images available in open-source databases with relatively small data volumes, and, hence, are not applicable for routine clinical practice. This research aims to integrate multiple deep learning algorithms and develop a system (DeFrame) that can be used to accurately detect intestinal polyps in real time during clinical endoscopy.

Although myocarditis and pericarditis were not observed as adverse events in coronavirus disease 2019 (COVID-19) vaccine trials, there have been numerous reports of suspected cases following vaccination in the general population. 

An artificial intelligence (AI) algorithm for prostate cancer detection and grading was developed for clinical diagnostics on biopsies. This study showed that a deep learning model not only can find and grade prostate cancer on biopsies comparably with pathologists but also can predict adverse staging and probability for recurrence after surgical treatment

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Artificial intelligence (AI), deep learning (DL), and machine learning (ML) have transformed many industries and areas of science. Now, these tools are being applied to address the challenges of cancer biomarker discovery, where the analysis of vast amounts of imaging and molecular data is beyond the ability of traditional statistical analyses and tools. In a special issue of Cancer Biomarkers, researchers propose various approaches and explore some of the unique challenges of using AI, DL, and ML to improve the accuracy and predictive power of biomarkers for cancer and other diseases.

Melanoma is by far the most serious form of skin cancer. But when it’s caught and treated early, the disease is almost always curable. That’s why it’s important to develop more effective ways to detect melanoma in its earliest stages — a key focus of research at Memorial Sloan Kettering and elsewhere. One approach that’s showing promise is the creation of artificial intelligence (AI) tools. For the past five years, investigators from MSK have led an annual Grand Challenge for the development of AI algorithms that can accurately distinguish between spots that are melanoma and those that are not.

El evento tendrá lugar el día lunes 25 de abril a las 19:30. Participan: Dra. Ana Santaballa Beltrán Jefa de la unidad de Cáncer de Mama y Tumores Ginecológicos, Hospital Universitari y Politècnic La Fe, Valencia. Dra. Virginia Martínez Marín MD PhD, Servicio de Oncología Médica, Hospital U. La Paz, Madrid Dr. Luis Ignacio Martínez Alcorta Servicio de Medicina Preventiva, Hospital Universitario Donostia. Una sesión moderada por Alipio Gutierrez, Responsable de Contenidos de Salud de Telemadrid.

Sarcopenia is defined as the loss of skeletal muscle mass and muscle function. It is common in patients with malignancies and often associated with adverse clinical outcomes. The presence of sarcopenia in patients with cancer is determined by body composition, and recently, radiologic technology for the accurate estimation of body composition is under development. Artificial intelligence (AI-) assisted image measurement facilitates the detection of sarcopenia in clinical practice.

 

A trained artificial intelligence model predicted treatment outcomes before surgery of women with high-grade serous ovarian cancer, according to study results. Researchers presented the findings of the pilot study in a plenary session during the Society of Gynecologic Oncology 2022 Annual Meeting on Women’s Cancer.