Hospital Information Management System

Should you have to totally depend yourself on conventional diagnosis system?Early diagnosis is the key strategy to tackle TB, this is done by “sputum testing” and “Molecular testing”. Unfortunately, sputum testing liable to miss the disease in its early stages, only having 50% accuracy rate. On the other hand, Molecular testing (highly accurate) is too costly for screening.Why you need of effective solution for early detection in current scenario?Chest radiograph seems to be promising solution but still lagging in the developing world, because of lack availability of medical expertise or trained radiologists to interpret images accurately.Here AI or Deep Learning Models play vital role to fulfill the gap in medical image interpretation with accuracy. The complete solution provides a potential platform to lead to earlier detection and game changer impact in battling against TB.   Our “Deep Learning Engine” behind early phase detection of TB: Our Deep Learning Engine is based on advanced neural network algorithms which are trained over a growing database of chest X-ray images considering different medical cases. The process is analogically similar to how one would train a radiologist. Final outcome of our engine assists doctors in fast-tracking of abnormalities and confirmatory diagnosis of TB patients.It is to be carefully noted here that it does not provide ground truth for confirming TB. This platform is developed to detects (most of the time) TB-related abnormalities in the lungs (scars, opacities, pleural effusion, etc.).Technical Specifications: Our Deep Learning Engine provides a probability score from 0 to 1 (considering all decimal/floating type values in-between) for signs of TB. For example, considering case of normal person, its probability score will be very closer to 0 or absolute 0 and vice-versa.Accuracy & Validation: All prediction is based on high quality input images which are feeds to model considering various type of abnormalities to boost accuracy and further validation is done by radiologist factual report. Benefits at a Glance:Facilitates to Identify and confirming TB at earlier stage.Can be fine-tuned for cluster of abnormalities that is found in specific geo-location.Prioritization of urgent cases based on rapid (nearly real-time) report/prediction result.Rapid diagnosis even in remote areas with cost-effectivenessFacilitates the quality and efficiency of radiology process over chest X-ray.Provides scalable and easily accessible TB examination to vulnerable populationOur Mission & Collaboration:Our mission is to simplify extraction of hidden fruitful information with improved precision of the radiology scanning process by embedding Deep Learning Engine to assist both patient (by providing facts in simple figure) & radiologist (to boost capabilities). Our data scientists working and doing research in collaboration with hospitals, in dimension of transforming healthcare in AI.