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18 AI Applications / Usecases / Examples in Healthcare in 2021


AI, computer vision and machine learning systems proved that machines are better and faster than humans analyzing big data. Today, organizations have large datasets of patient data and insights about diseases through techniques like Genome Wide Association Studies (GWAS). Using AI, healthcare providers can analyze and interpret the available patient data more precisely for early diagnosis and better treatment. Today, it is possible to say whether a person has the chance to get cancer from a selfie using computer vision and machine learning to detect increased bilirubin levels in a person's sclera, the white part of the eye. As the interest in AI in the healthcare industry continues to grow, there are numerous current AI applications, and more use cases will emerge in the future.

Analysis of multispectral imaging with the AstroPath platform informs efficacy of PD-1 blockade


Immunohistochemical stains for individual markers revolutionized diagnostic pathology decades ago but cannot capture enough information to accurately predict response to immunotherapy. Newer multiplex immunofluorescent technologies provide the potential to visualize the expression patterns of many functionally relevant molecules but present numerous challenges in accurate image analysis and data handling, particularly over large tumor areas. Drawing from the field of astronomy, in which petabytes of imaging data are routinely analyzed across a wide spectral range, Berry et al. developed a platform for multispectral imaging of whole-tumor sections with high-fidelity single-cell resolution. The resultant AstroPath platform was used to develop a multiplex immunofluorescent assay highly predictive of responses and outcomes for melanoma patients receiving immunotherapy. Science , aba2609, this issue p. [eaba2609][1] ### INTRODUCTION New therapies have been designed to stimulate the host’s immune system to fight cancer. Despite these exciting, recent successes, a large proportion of patients still do not respond to anti–programmed cell death-1 (PD-1) or anti–programmed death ligand-1 (PD-L1) therapies, and thus, biomarkers for patient selection are highly desirable. The only U.S. Food and Drug Administration–approved histopathology biomarker tests for anti–PD-1 or anti–PD-L1 therapy is assessment of PD-L1 protein expression by means of immunohistochemistry. This approach is unidimensional and has limitations. Innovative characterization of the tumor microenvironment (TME) with a focus on multidimensional, spatially resolved interactions at the single-cell level will provide critical mechanistic insights into therapeutic responses and potentially identify improved biomarkers for patient selection. Using multispectral approaches to image the TME and substituting cells for stars and galaxies, we applied the methodology and infrastructure developed for astronomy to pathologic analysis of specimens from patients with melanoma. ### RATIONALE The next generation of pathologic analyses will require platforms that can characterize the coexpression of key molecules on specific cellular subsets in situ and spatial relationships between tumor cells and multiple immune elements. To that aim, we applied astronomical algorithms for high-quality imaging and the establishment of relational databases to multiplex immunofluorescence (mIF) labeling of pathology specimens, facilitating spatial analyses and immunoarchitectural characterization of the host-tumor interface. In all, we curated and coordinately mapped six markers, both individually and in combination in tumor tissue from 98 patients with melanoma receiving anti–PD-1 therapy. This dataset comprised ~127,400 image mosaics composed of more than 100 million single cells. The data outputs were linked to patient outcomes, informing in a clinically relevant way how cancer evades the immune system and potentiating biomarker assay development for precision immunotherapy. ### RESULTS The imaging protocols used in this study were used to address outstanding questions regarding the impact of high-power field sampling strategies on biomarker performance. This information was then used to develop an approach for operator-independent field selection. The image handling strategies also facilitated the robust assessment of the intensity of PD-1 and PD-L1 expression in situ (negative, low, mid, and high levels) on different cell types. Thus, with only six markers (PD-1, PD-L1, CD8, FoxP3, CD163, and Sox10/S100), we were able to develop 41 combinations of expression patterns for these molecules and map relatively rare cells such as CD8+FoxP3+ cells to the tumor stromal boundary. Moreover, a high density of CD8+FoxP3+PD-1low/mid cells was closely associated with response to PD-1 blockade. Cell types associated with a lack of response to therapy were also identified—for example, CD163+ macrophages that were PD-L1–. This latter phenotype was also found to have a negative effect on long-term survival. When these and other key cell phenotype densities were combined, they were highly predictive of objective response and stratified long-term patient outcomes after anti–PD-1–based therapies in both a discovery cohort and an independent validation cohort. ### CONCLUSION Here, we present the AstroPath platform, an end-to-end pathology workflow with rigorous quality control for creating quantitative, spatially resolved mIF datasets. Although the current effort focused on a six-plex mIF assay, the principles described here provide a general framework for the development of any multiplex assay with single-cell image resolution. Such approaches will vastly improve the standardization and scalability of these technologies, enabling cross-site and cross-study comparisons. This will be essential for multiplex imaging technologies to realize their potential as biomarker discovery platforms and ultimately as standard diagnostic tests for clinical therapeutic decision-making. ![Figure][2] Strong parallels between multispectral analyses in astronomy and emerging multiplexing platforms for pathology. The next generation of tissue-based biomarkers are likely to be identified by use of large, well-curated datasets. To that end, image analysis approaches originally developed for astronomy were applied to pathology specimens to produce trillions of pixels of robust tissue imaging data and facilitate assay and atlas development. IMAGES: (BENEATH “ASTRONOMY”) SLOAN DIGITAL SKY SURVEY; (MICROSCOPE) AKOYA BIOSCIENCE Next-generation tissue-based biomarkers for immunotherapy will likely include the simultaneous analysis of multiple cell types and their spatial interactions, as well as distinct expression patterns of immunoregulatory molecules. Here, we introduce a comprehensive platform for multispectral imaging and mapping of multiple parameters in tumor tissue sections with high-fidelity single-cell resolution. Image analysis and data handling components were drawn from the field of astronomy. Using this “AstroPath” whole-slide platform and only six markers, we identified key features in pretreatment melanoma specimens that predicted response to anti–programmed cell death-1 (PD-1)–based therapy, including CD163+PD-L1– myeloid cells and CD8+FoxP3+PD-1low/mid T cells. These features were combined to stratify long-term survival after anti–PD-1 blockade. This signature was validated in an independent cohort of patients with melanoma from a different institution. [1]: /lookup/doi/10.1126/science.aba2609 [2]: pending:yes



Health care services in Africa are under-resourced & overused. There are only 20 pediatric radiologists in Africa, where pneumonia is the #1 cause of death in children under 5. Covid-19, tuberculosis and cancer are an increased burden on doctors globally and is another threat to our already fragile healthcare system. Misdiagnosis is common and human error can result in increased medical legal exposure to doctors. RADIFY AI for mammography assists with early detection of breast cancer. Radify AI for ultrasound is a point of care solution for detection of chest & breast diseases.

Clinical trials are better, faster, cheaper with big data

MIT Technology Review

"One of the most difficult parts of my job is enrolling patients into studies," says Nicholas Borys, chief medical officer for Lawrenceville, N.J., biotechnology company Celsion, which develops next-generation chemotherapy and immunotherapy agents for liver and ovarian cancers and certain types of brain tumors. Borys estimates that fewer than 10% of cancer patients are enrolled in clinical trials. "If we could get that up to 20% or 30%, we probably could have had several cancers conquered by now." Clinical trials test new drugs, devices, and procedures to determine whether they're safe and effective before they're approved for general use. But the path from study design to approval is long, winding, and expensive.

Auransa and POLARISqb enter research collaboration finding treatments for neglected women's diseases


Auransa Inc., an artificial intelligence (AI) company developing precision medicines in areas of unmet medical needs, and Polaris Quantum Biotech (POLARISqb), a quantum drug design company, announced a research collaboration addressing therapeutics for neglected diseases disproportionately affecting women. The partnership seeks to discover treatments that may tackle many such diseases, and their complementary expertise promises to seek solutions that elude medical research. Auransa is an AI-driven biotech company, with a pipeline of novel compounds for various diseases. Auransa's proprietary predictive computational platform, SMarTR Engine, uses computational approaches to tackle disease heterogeneity to predict targets and compounds, generating insights from molecular data. POLARISqb built the first drug discovery platform using quantum computing, making the process ten times faster.

Artificial Intelligence in the Pharma Industry: Clinical Trials


Artificial Intelligence has played an increasingly important role within the pharmaceutical space especially with recent restrictions due to COVID-19. The drug development process can be lengthy and costly but many companies have begun implementing AI into their clinical trials to speed up patient on-site visits, test efficacy and bring more drugs to market. As we discussed previously, AI has played an important role in the discovery process. Now let's take a look at AI in clinical trials… PRNewswire reports the global virtual clinical trials market size is expected to reach 11.5 billion USD by 2028 with a compound annual growth rate of 5.7% from 2021 to 2028 according to Grand View Research, Inc. The growth in the virtual clinical trial space is directly related to the need for an increase in patient diversity and an increase in the number of decentralized/virtual trials due to the impact of COVID-19.

Mendel raises $18M to tease out data structure from medicine's disparate document trove – TechCrunch


The medical industry is sitting on a huge trove of data, but in many cases it can be a challenge to realize the value of it because that data is unstructured and in disparate places. Today, a startup called Mendel, which has built an AI platform both to ingest and bring order to that body of information, is announcing $18 million in funding to continue its growth and to build out what it describes as a "clinical data marketplace" for people not just to organize, but also to share and exchange that data for research purposes. It's also going to be using the funding to hire more talent -- technical and support -- for its two offices, in San Jose, CA and Cairo, Egypt. The Series A round is being led by DCM, with OliveTree and MTVLP, and previous backers Launch Capital, SOSV, Bootstrap Labs and Chairman of UCSF Health Hub Mark Goldstein also participating. The funding comes on the heels of what Mendel says is a surge of interest among research and pharmaceutical companies in sourcing better data to gain a better understanding of longer-term patient care and progress, in particular across wider groups of users, not just at a time when it has been more challenging to observe people and run trials, but in light of the understanding that using AI to leverage much bigger data sets can produce better insights.

Machine Learning


The following information is listed on the source website. Membranolytic anticancer peptides (ACPs) are drawing increasing attention as potential future therapeutics against cancer, due to their ability to hinder the development of cellular resistance and their potential to overcome common hurdles of chemotherapy, e.g., side effects and cytotoxicity. This dataset contains information on peptides (annotated for their one-letter amino acid code) and their anticancer activity on breast and lung cancer cell lines. Two peptide datasets targeting breast and lung cancer cells were assembled and curated manually from CancerPPD. Linear and l-chiral peptides were retained, while cyclic, mixed or d-chiral peptides were discarded.

'Electronic nose' sniffs out cancer in blood samples - Futurity


You are free to share this article under the Attribution 4.0 International license. An odor-based test that sniffs out vapors emanating from blood samples was able to distinguish between benign and pancreatic and ovarian cancer cells with up to 95% accuracy, according to a new study. The findings suggest that the tool--which uses artificial intelligence and machine learning to decipher the mixture of volatile organic compounds (VOCs) emitting off cells in blood plasma samples--could serve as a noninvasive approach to screen for harder-to-detect cancers, such as pancreatic and ovarian. "It's an early study but the results are very promising," says A.T. Charlie Johnson, a professor of physics and astronomy at the University of Pennsylvania. "The data shows we can identify these tumors at both advanced and the earliest stages, which is exciting. If developed appropriately for the clinical setting, this could potentially be a test that's done on a standard blood draw that may be part of your annual physical."

Deepak Chopra Plans To Live Forever Through AI, Here's How


In the months leading up to the pandemic, 73 year old best-selling author Deepak Chopra uploaded his "consciousness" to the AI Foundation to ensure he would be around to chat with future generations. Virgin Galactic founder Richard Branson, Twitter co-founder Biz Stone, and venture capitalist Cyan Bannister did the same. Now they see a future filled with personalized AI for all. In an interview with Chopra, the meditation guru told me his goal is to help a billion people with his AI. Stone messaged that he is training his AI to tell jokes as it has been speaking at conferences for him, and Bannister lets her AI vet founders' pitches.