AI Powered Anatomic Pathology Software that provides better Data to save lives

 

AI Powered Anatomic Pathology Software that

 provides better Data to save lives

Madabhushi A., Feldman M. D., Leo P conducted a deep learning approach for the Gleason classification of prostate biopsies. The research is done by computer pathology with clinical degrees using supervised deep learning of whole slide images and Virtual histological staining of unlabeled tissue by autofluorescence imaging with deep learning.

             For more than a decade, Deep Lens has been expanding to one of the world's first digital cloud platforms for pathology, enabling pathology groups to collaborate on breakthrough cancer research in dozens of cancers, including artificial image recognition workflows, telepathology collaboration support, cloud storage and integrated API integrations with hardware and software vendors and biopharmaceutical companies. Beijing Infervision is an artificial intelligence and high-tech company dedicated to the application of deep learning technologies to support medical imaging and diagnostics with efficient and precise solutions. RenalytixAI^TM is a developer of artificial intelligence enabled clinical diagnostics for kidney disease.

Orbita provides software to improve patient engagement in digital healthcare with voice-based AI solutions. Proscia, a leading provider of digital pathology software released DermAI® today - the first of its series of AI applications to advance the practice of pathology.

To date, all attempts to apply AI to pathology have been developed in isolated development environments with toy datasets. Proscia’s DermAI® uses deep learning to read hundreds of variants of skin disease and classify them in pre-diagnostic categories to increase confidence, quality and speed of pathology. As a module of Proscia’s Concentriq®platform, DermAI®, the first of its series of AI applications advances the practice of pathology, uses in-depth learning for screening and classification of skin biopsies to reduce costly errors and improve lab quality and efficiency while the number of physicians entering the field of pathology continues to decline.

DermAI® allows dermatopathology laboratories additional insights into the laboratory work they perform. Many medical professionals deal with anatomical pathology that in some cases makes the work more complicated and is an excellent candidate for benefit in terms of reducing errors, reducing delay, improving communication and other benefits of automation. At Computer Trust Corporation, we develop laboratory pathology software as a service to provide more accurate diagnoses.

The examination of the sample, the preparation of slides with oil stains and water-based tissue, the reading of slides to obtain additional stains and the gradual recording of findings require a small army of experts: gross technologists, pathologists and medical transcriptionists. They treat the specimen in one way or another and work together to collect data for important insights. Computer Trust Corporation has developed data technology to support this entire laboratory process.

Data management and automated software lay the groundwork to enable this small army of experts from highly skilled technologists and pathologists to medical transcriptionists to communicate and collaborate. FHIR is crucial for the future of interoperability because it helps health organizations not only connect data sources but also collect actionable data from the ever-growing stream of health information. Once they have tapped this data, they can use technology to pool member cohorts into uniform care records.

Immediate access to clean, specific data from thousands of provider sites represent a paradigm shift in healthcare. Access to clinical data is no longer a pipe dream for health plans but something they can focus and address together. In short, more relevant data and better presentation of this data will make doctors provide more accurate diagnoses.

In the diagnostic discipline of imaging, both clinicians and laboratory physicians are working to make new levels of precision a reality. At RSNA 2016 the idea of broadening the clinical context and the need for radiologists to improve the accuracy of their work was a central theme in the forum.

        Dr. Eliot Siegel, the Director of Imaging at VA Maryland Healthcare System and Deputy Chairperson of Radiology at University of Maryland School of Medicine, looks at what the precision medicine looks like for radiologists. TrialJectory’s mission is to transform the world of cancer treatment using artificial intelligence.

The live panel reviewed the results of a May 2021 survey on factors that are important for patients and members to interact with health organizations. The panel presented actionable strategies to increase patient and member retention, restore revenue, implement solutions to reduce friction across multiple channels and prioritize care.

Because EHRs generate more health data than lower quality data for secondary applications such as population health, precision medicine and pandemic management, data collection puts a strain on physicians and data entry workers. Speakers will explore ways to reduce EHR burden on physicians, describe the importance of standardized and harmonious data, suggest quality measures and strategies that need to be changed and advocate a reassessment of clinical data collection as a whole.

The ordering process which initiates tissue collection and subsequent pathological examination as well as the results report is described in the Digital Pathology Ordering and Report Integration Profile and is intended to support the ordering of reports for both clinical and anatomical pathology. The ordering process requires two transactions derived from a combination of the original transaction (Laboratory 3) for clinical pathology (Pat 1 and Pat 3) and the former for anatomical pathology. This ordering process is Laboratory 1 and Laboratory 3 and includes: pathological examination, subsequent acquisition of digital assets, digital processing steps, details of digital pathological image order and integration profile (intended for future development), acquisition of digital image, coverage of a minimum number of stakeholder transactions necessary for preservation of digital images, storage in DICOM format, notification to relevant stakeholders and availability.