The healthcare client wanted to address their inefficiency in diagnosis and Sparity implemented a deep-learning AI model using convolutional neural networks (CNNs) to analyze medical data based on the symptoms presented by individuals and their genetics.

The client is a diversified provider of quality and individualized healthcare service that researches, develops and delivers innovative medical care solutions into cancer care to treat millions of patients each year. The client is committed to improving the health and well-being of the people in their community. It provides solutions that remarkably improve how healthcare providers diagnose, manage and monitor medical care to improve patient outcomes.

Overview

The healthcare client was looking for a solution that could address their severe need to efficiently sort through the presented medical data, analyze it, and accurately diagnose the medical condition. Furthermore, they needed a solution that could enhance their clinical diagnostics, improve accuracy in early detection resulting in improvement in their treatment capabilities. Sparity acted quickly and developed a deep-learning AI model and implemented convolutional neural network (CNN) architecture with deep learning algorithms that utilizes the physical traits of the patient, along with a database of information to provide a more accurate and efficient diagnosis for the patients, thereby saving valuable time and speeding up the treatment process.

Challenges

The healthcare client needed a digital engineering partner to address the key challenge of early diagnosis of the disease to assist clinical practice. The client required a solution that could analyze the collected structured data such as physical exam results, symptoms, basic metrics, medications, disease-specific data, diagnostic imaging, gene expressions, and different laboratory testing for the rapid identification, precise and early diagnosis of disease in patient to facilitate subsequent clinical patient management. Furthermore, it needed a solution for the identification of Diabetic Retinopathy, Metastasis detection and Cardiovascular Risk Factors prediction. The current diagnosis was cost-intensive, error-prone and was based upon one or a few doctors’ opinions.

Implementation

A framework to automatically detect and localize tumors as small as 100 ×100 pixels in giga-pixel microscopy images sized 100, 000 ×100, 000 pixels.

Retinal Fundus images from the input of a deep neural network consisting of residual blocks, an attention layer to learn the most predictive eye features, to predict cardiovascular risk factors.

Solution

Sparity’s extensive experience in large healthcare organizations allowed it to gain insight into the customer’s requirements immediately and developed a deep-learning AI model with convolutional neural network (CNN) architecture to analyze the medical data based on their presented symptoms and genetic history. As part of the solution, deep-learning system churns through different kinds of large data sets, such as CT scans, genetic sequences and treatment histories for prediction, screening, analysis, and interpretation of tumor-imaging and drug discovery and validation in clinical settings. Furthermore, the solution’s advanced pattern recognition capabilities make it more capable of spotting anomalies than most human healthcare professionals assisting them in early diagnosis, enhancing physicians’ diagnostic skills, help clinical decision making and offer advanced treatment plans.

Key Deliverables

Economical

The solution significantly reduces the overall operational cost in the long run and help maximize their returns

Accuracy

Improved diagnosis accuracy to limit or even reverse the trend that characterizes the diffusion of such diseases.

Automation

Automated grading increases efficiency, reproducibility, coverage of screening programs, & reducing barriers to access.

Effectiveness

ML utilizes the knowledge of multiple doctors to develop a diagnosis to improve patient outcomes by early detection and treatment.

Let’s get started

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