Chaith Kondragunta, CEO, AIRA MATRIX, speaks with Pharmabiz.com
AIRA MATRIX CEO, Chaith Kondragunta shares key insights on how Deep learning-based solutions can significantly change outcomes of cancer therapy.
TRANSFIN Podcasts: Gearing Up for an AI Future with Chaith Kondragunta of AIRA Matrix
Chaith Kondragunta, CEO, AIRA MATRIX, speaks with Anaytics Insight
AIRA MATRIX CEO, Chaith Kondragunta shares key insights on how our Deep Learning-based applications improve workflow efficiency and enable informed decision-making in the healthcare and pharma industries.
ARVO Imaging in the Eye Conference
AIRA MATRIX is happy to share that our submissions, “LEAP: LEsion-Aware Prediction of diabetic macular edema grades from color fundus images using deep learning”, and “Deep Learning-Based Ocular Disease Classification using Retinal Fundus Images” have been accepted for presentation at the 2021 ARVO IMAGING IN THE EYE conference.
The first paper will shed light on how our Deep Learning-based LEsion-Aware Prediction (LEAP) model effectively performs automatic detection and grading of Diabetic Macular Edema (DME). The model captures the interclass variability across DME grading, which can be highly impactful for screening programs worldwide.
The second paper will share key insights on how our Deep Learning algorithms with an ensemble of CNN models are capable of detecting and diagnosing diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD), and other pathologies.
AIRA MATRIX and Pharmaseed announces collaboration
AIRAMatrix Pvt. Ltd. (“AIRA Matrix”), the premier provider of Artificial Intelligence (AI) products and services for the Life Sciences industries and Pharmaseed Ltd (“Pharmaseed”), Israel’s largest GLP-certified pre-clinical CRO specializing in translational and regenerative studies announced a strategic collaboration for developing AI applications for quantification of histological parameters derived from in-vivo disease models in animal studies.
Pathology AI Algorithms Deployed on Augmented Reality Microscope in Preclinical Study
Life sciences Artificial Intelligence products and services company, AIRA Matrix (“AIRA Matrix”), and microscope-based digital pathology platform Augmentiqs (“Augmentiqs”), announced the world’s first pre-clinical deployment of deep-learning algorithms in an augmented reality microscope.
AIRA Matrix announces webinar series on AI applications in Reproductive Toxicology
AIRA Matrix announces a webinar series on AI applications in Reproductive Toxicology. The first in the series, “Application of Artificial Intelligence for Spermatogenic Staging Assessment in Rodents” will be presented on Feb 11, 2021, in association with Dr. Dianne Creasy (Ph.D., DipRCPath (Tox), FRCPath). Dr. Creasy is a leading toxicologic pathologist and an expert in reproductive toxicology reporting.
AIRA MATRIX Invites You to a Luncheon Session: “Demystifying Deep Learning for Pathologists” at the 17th European Congress of Toxicologic Pathology
AIRA MATRIX is pleased to announce that it will be hosting a session titled, “Demystifying Deep Learning for Pathologists” on September 18th, 2019 at the 17th European Congress of Toxicologic Pathology organized by European Society of Toxicologic Pathology (ESTP).
AIRA Matrix CEO Chaith Kondragunta to deliver speech at Intelligent Health Inspired Online Summit
AIRA Matrix CEO, Chaith Kondragunta will be speaking at Intelligent Health Inspired, the world’s largest online summit dedicated to AI in medicine. The session titled, “AI Applications for Screening and Early Disease Detection in Pathology” is a part of the AI Innovation series.
Webinar | Application of Artificial Intelligence in Scoring Rodent Cardiomyopathy
Progressive cardiomyopathy (PCM) is a common background change seen in rodent toxicology. We invite you to participate and discuss deep learning applications for progressive cardiomyopathy (PCM) scoring in rodents.
AIRA Matrix Wins Second Place in the Global Gleason 2019 Challenge at MICCAI2019
AIRA Matrix wins second place in the global Gleason 2019 Challenge at MICCAI2019. The challenge involved automating the Gleason score, a highly reliable grading method for classifying the histologic characteristics of Prostate cancer. Our proposed deep learning algorithm will help improve the accuracy, precision and speed of Gleason’s scoring. These improvements, will enable better prognosis of prostate cancer and help guide the right therapies.