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A Deep Learning Tool Can Help Avoid False Positive Breast Cancer Scans


A Deep Learning Tool Can Help Avoid False Positive Breast Cancer Scans

Using a deep learning tool can help improve accuracy and reduce false positives during magnetic resonance imaging (MRI) scans to check for breast cancer, shows research led by New York University

Personalised medicine and the advantages of big data and AI-based diagnostics


Artificial intelligence (AI) and big data are transforming healthcare with high-throughput analyses of complex diseases. Machine learning and sophisticated computational methods can be used to efficiently interpret human genomes and other biomarkers, providing insights for patient treatment and with major applications in diagnostics and preventive care.


From a small village in China to MD Anderson: Genomic medicine researcher looks to the future of big data in cancer care


As an associate professor in Genomic Medicine, Linghua Wang, M.D., Ph.D., studies how normal cells become cancerous and how cancer cells develop resistance to drugs.

Wang believes the rise in data science, machine learning and artificial intelligence will advance precision and predictive oncology and accelerate drug development.

Automated detection of microsatellite status in early colon cancer by infrared imaging combined with artificial intelligence


The researchers of PRODI (Center for Protein Diagnostics, Ruhr-University Bochum, Germany) used tissue sections from the prospective multicentre AIO ColoPredictPlus 2.0 registry study to verify the novel label-free infrared (IR) imaging combined with artificial intelligence (AI) for tumour detection and classification of microsatellite status.

The 2021 landscape of FDA-approved artificial intelligence/machine learning-enabled medical devices: An analysis of the characteristics and intended use


Machine learning (ML), a type of artificial intelligence (AI) technology that uses a data-driven approach for pattern recognition, has been shown to be beneficial for many tasks across healthcare. To characterize the commercial availability of AI/ML applications in the clinic, we performed a detailed analysis of AI/ML-enabled medical devices approved/cleared by the US Food and Drug Administration (FDA) by June 2021.

AI tool allows clinicians to make optimal, personalized chemotherapy doses for patients


A team of researchers from National University of Singapore (NUS), in collaboration with clinicians from the National University Cancer Institute, Singapore (NCIS) which is part of the National University Health System (NUHS), has reported promising results in using CURATE.AI, an artificial intelligence (AI) tool that identifies and better allows clinicians to make optimal and personalized doses of chemotherapy for patients.

New CRP: The Potential of E-Learning Interventions for AI-assisted Contouring Skills in Radiotherapy (E33046)


The IAEA is launching a new Coordinated Research Project (CRP) aimed at investigating the potential of artificial intelligence (AI) to enhance contouring skills in radiotherapy, especially focusing on increasing accuracy of delineation of organs at risk in head and neck cancers. Radiation oncology has evolved rapidly in recent decades in terms of innovations in treatment equipment, volumetric imaging, information technology and increased knowledge in cancer biology. New delivery technologies and associated imaging modalities have enabled highly optimized precision radiation therapy and contributed to improvements in tumor control and cancer patient cure.

AI is secret weapon for cancer trial recruitment and engagement, says TrialJectory CEO


Artificial intelligence can remove barriers to participation, aid patient engagement and boost enrollment rates in cancer trials, according to an analysis by tech firm TrialJectory. TrialJectory shared the research in a poster presentation at the American Society of Clinical Oncology (ASCO) annual meeting this week, explaining that its AI-based approach had outperformed traditional methods.

A Machine Learning-Based System for Real-Time Polyp Detection (DeFrame): A Retrospective Study


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.

AI Model for Prostate Biopsies Predicts Cancer Survival


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

AI and machine learning could improve cancer diagnosis through biomarker discovery


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.

Can Artificial Intelligence Detect Melanoma?


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.

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