In data science and machine learning, simplicity is an important concept that can have significant impact on model characteristics such as performance and interpretability. Over-engineered solutions tend to adversely affect these characteristics by increasing the likelihood of overfitting, decreasing computational efficiency, and lowering the transparency of the model's output. The latter is particularly important for areas that require a certain degree of interpretability, such as medicine and healthcare, finance, or law. The inability to interpret and trust a model's decision -- and to ensure that this decision is fair and unbiased -- can have serious consequences for individuals whose fate depends on it. This article aims to highlight the importance of giving precedence to simplicity when it comes to implementing a data science or machine learning solution.
Earlier this month, healthcare artificial intelligence (AI) company Paige announced a new partnership with renowned technology giant, Microsoft. Paige describes itself as a company at the forefront of technology and healthcare, especially in the field of cancer diagnostics and pathology. The company explains its mission: "Led by a team of experts in the fields of life sciences, oncology, pathology, technology, machine learning, and healthcare…[we strive] to transform cancer diagnostics. We make it possible not only to provide additional information from digital slides to help pathologists perform their diagnostic work efficiently and confidently, but also to go beyond by extracting novel insights from digital slides that can't be seen by the naked eye. These unique tissue signatures have the potential to help guide treatment decisions and enable the development of novel biomarkers from tissues for diagnostic, pharmaceutical and life sciences companies."
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Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The European Union on Monday launched a project to collect and aggregate cancer imaging data in an effort to speed up innovation and early cancer diagnosis using artificial intelligence. The new European Cancer Imaging Initiative will give clinicians, researchers and innovators "easy access to large amounts of cancer imaging data", the European Commission said in a statement. "A cross-border, interoperable, and secure infrastructure that will preserve privacy will speed up innovation in medical research. For example, it will be possible to train new technologies that use artificial intelligence (AI) on a large dataset."
The name Sybil has its origins in the oracles of Ancient Greece, also known as sibyls: feminine figures who were relied upon to relay divine knowledge of the unseen and the omnipotent past, present, and future. Now, the name has been excavated from antiquity and bestowed on an artificial intelligence tool for lung cancer risk assessment being developed by researchers at MIT's Abdul Latif Jameel Clinic for Machine Learning in Health, Mass General Cancer Center (MGCC), and Chang Gung Memorial Hospital (CGMH). Lung cancer is the No. 1 deadliest cancer in the world, resulting in 1.7 million deaths worldwide in 2020, killing more people than the next three deadliest cancers combined. "It's the biggest cancer killer because it's relatively common and relatively hard to treat, especially once it has reached an advanced stage," says Florian Fintelmann, MGCC thoracic interventional radiologist and coauthor on the new work. "In this case, it's important to know that if you detect lung cancer early, the long-term outcome is significantly better. Your five-year survival rate is closer to 70 percent, whereas if you detect it when it's advanced, the five-year survival rate is just short of 10 percent."
Cancer is a disease characterized by the abnormal growth and division of cells in the body. Tumors can affect any part of the body and can be benign (non-cancerous) or malignant (cancerous), spreading to other parts of the body through the bloodstream or lymph system. Scientists at the University of California, San Francisco (UCSF) and IBM Research have created a virtual library of thousands of "command sentences" for cells using machine learning. These "sentences" are based on combinations of "words" that direct engineered immune cells to find and continuously eliminate cancer cells. This research, which was recently published in the journal Science, is the first time that advanced computational techniques have been applied to a field that has traditionally progressed through trial-and-error experimentation and the use of pre-existing molecules rather than synthetic ones to engineer cells.
An AI-based home screening test to detect oral and throat cancers from saliva samples is now available in the United States with the hope of transforming oral and throat cancer detection. Based on a technology approved by the US Food and Drug Administration (FDA) as a "breakthrough device," the saliva test can detect early symptoms of oral and throat cancer with more than 90 percent accuracy. Due to a lack of effective diagnostic tools, these cancers often go undiagnosed until they have reached an advanced stage, resulting in low survival rates. In a previous study, Maria Soledad Sosa from the Icahn School of Medicine at Mount Sinai and Julio A. Aguirre-Ghiso, now at Albert Einstein College of Medicine, discovered that the ability of cancer cells to remain dormant is controlled by a protein called NR2F1. This receptor protein can enter the cell nucleus and turn numerous genes on or off to activate a program that prevents the cancer cells from proliferating.
Accurate and repeatable measurement of high-grade glioma (HGG) enhancing (Enh.) and T2/FLAIR hyperintensity/edema (Ed.) is required for monitoring treatment response. We aim to develop an HGG volumetric measurement and visualization AI algorithm that is generalizable and repeatable. A single 3D-Convoluted Neural Network, NS-HGlio, to analyze HGG on MRIs using 5-fold cross validation was developed using retrospective (557 MRIs), multicentre (38 sites) and multivendor (32 scanners) dataset divided into training (70%), validation (20%), and testing (10%). Six neuroradiologists created the ground truth (GT). Ed. (WholeLesion/WL) tumor tissue and repeatability testing on 14 subjects from the TCIA MGH-QIN-GBM dataset using volume correlations between timepoints were performed. NS-HGlio is accurate, repeatable, and generalizable.
According to a recent study from Karl Landsteiner University of Health Sciences (KL Krems), using radiomics and deep learning algorithms can quickly and accurately distinguish between glioblastoma (primary tumors) and brain metastases. The study, which was published in Metabolites, discovered that magnetic resonance-based radiological data of tumor oxygen metabolism provide a solid foundation for discrimination via neural networks. This combination of oxygen metabolic radiomics and AI analysis was discovered to be vastly superior to human expert evaluations in all critical criteria, even when essential oxygen values did not differ significantly between tumor types. The neural networks' ability to make clear distinctions based on these values demonstrates the method's potential. Glioblastoma (GB) and brain metastasis (BM) are the most commonly occurring types of brain tumors in adults.
New research uses AlphaFold, an artificial intelligence (AI)-powered protein structure database, to accelerate the design and synthesis of a drug to treat hepatocellular carcinoma (HCC), the most common type of primary liver cancer. It is the first successful application of AlphaFold to hit identification process in drug discovery. This study by an international team of researchers, published last week in Chemical Science, is led by the University of Toronto's Acceleration Consortium director Alán Aspuru-Guzik, Chemistry Nobel laureate Michael Levitt, and Insilico Medicine founder and CEO Alex Zhavoronkov. AI is revolutionizing drug discovery and development. In 2022, the AlphaFold computer program, developed by Alphabet's DeepMind, predicted protein structures for the whole human genome––a remarkable breakthrough in both AI applications and structural biology.