CASE STUDY

The Deep Learning AI model showed a 25% reduction of false positives in localize & metastasis detection 

The healthcare client aspired to revive and enhance its diagnosis and screening approach and chose Sparity for developing a solution that would enable early and accurate diagnosis, thus advancing effective treatment. 

About Client

The Healthcare client is a US-based highly renowned multi-specialty facility that offers tertiary and quaternary care services, highly specialized treatments along with exceptional and compassionate patient care. The healthcare client brings together a variety of wellness care, diagnostic, and treatment services, all provided within a single location. 

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

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A framework to automatically detect and localize tumors 

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Trained the AI model on a pre-sampled set of image patches  

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Trained a random forest classifier for identification and prediction 

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Pixel-accurate annotation renders detection and localization

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 and deep learning algorithms of Artificial Intelligence 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.

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Key Deliverables

State-of-the-art sensitivity 

 Detection sensitivity increased to 60% while cutting detection time by half 

CapEx 

Reaching large number of patients and foster long-term relationships 

Early detection 

60% increase in early and accurate detection in localize & metastasis cases 

Cost effective 

15% reduction in cost while accommodating more requests

CLIENT RESULTS

The impact we’ve had

At Sparity, we’ve served as a strategic partner for hundreds of the fastest-growing B2B companies.

Learn more about how we help our clients accelerate their growth.

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