Applied deep learning & computer vision techniques to medical images

Company Information

A forward-thinking healthcare provider aimed to enhance diagnostic accuracy and efficiency through the advanced analysis of medical images. ProCogia was brought on board to apply deep learning and computer vision techniques, setting a new standard in image classification, segmentation, and object detection. This initiative promised to transform the way medical conditions, including coronavirus infections and heart attacks, were diagnosed, leveraging technology to improve patient outcomes and reduce healthcare costs.

The Challenge

The project’s challenges were manifold, involving the analysis of thousands of medical images to identify and classify various health conditions accurately. Key obstacles included ensuring the quality of clinical images for analysis, developing robust models capable of automating complex tasks such as image segmentation and object detection, and achieving high accuracy in diagnosis to minimize the reliance on more invasive and costly diagnostic tests.

Procogia’s Approach

ProCogia collaborated with leaders in the client’s organization to build the solution. Our process included:

Our team analyzed clinical images, all radiographs were initially screened for quality control and all low-quality or unreadable scans were removed. A set of 5,000 high-quality chest X-ray images were analyzed for the coronavirus project. A series of 10,000 microscopic images were analyzed for heart attack research.

By developing a ConvNET algorithm for object recognition/tracking from image data, we used machine learning techniques to predict the nature of particles.

We developed a deep neural network (U-net) to automate image segmentation.

Our team used various Machine Learning (ML) techniques for different classification tasks such as SVM, random forest and logistic regression, and checked for better evaluation metrics.

The Results

Costs were reduced by lowering the numbers of required lab tests/experiments; for example, the goal of the coronavirus project was to predict based on inexpensive chest X-rays and as a result, saved the cost of expensive tests such as CT and also reduced the cost of the Patients’ length of stay in ICUs by early diagnosis (On average this model could save approximately $2,000 and 7 days of ICU stay per patient).

Clinicians and scientists were provided with more insights to assist them in making better decisions about medical treatments and underlying conditions.

We made case-control study predictions based on the medical images with encouraging accuracy; for example, the prediction of serious coronavirus illness based on a chest X-ray without requiring more lab tests.

Services Used

Data Science

We use open source technology to leverage the full potential of your data. Predictive and prescriptive results are actioned using AI and Machine Learning (ML).

Bioinformatics

We deliver scientific results that drive clinical and translational research decisions. Our Bioinformatics team has extensive experience designing, optimizing, executing and analyzing pre-clinical and clinical research projects using next-generation sequencing technologies.

Conclusion

Through its collaboration with the healthcare provider, ProCogia showcased the transformative potential of applying deep learning and computer vision to medical imaging. This initiative not only improved diagnostic accuracy and efficiency but also contributed to substantial cost savings and enhanced patient care. By pushing the boundaries of medical imaging analysis, ProCogia has set new benchmarks in the healthcare industry, demonstrating the power of technology to drive better health outcomes and more efficient use of resources. This case study underscores ProCogia’s capability to deliver innovative solutions that address complex challenges in healthcare and beyond.

Explore more stories

Dig deeper into data development by browsing our blogs…

Custom Logging System for Plumber API and Shiny Applications A futuristic dashboard showing a central server connected to multiple API endpoints. Lines representing data flow between endpoints show user actions and responses, with real-time monitoring screens and storage for log files. On the left, warning icons indicate error tracking, while on the right, a clock symbolizes automated log management. The background has code snippets and data logs, creating a technical, blue-toned atmosphere that reflects system performance monitoring and troubleshooting.

Custom Logging Plumber API

Introduction Our client, a leading investment consulting firm, aimed to improve system monitoring, troubleshooting, and reliability by implementing a logging system that tracks user actions

Watch now ->

Get in Touch

Let us leverage your data so that you can make smarter decisions. Talk to our team of data experts today or fill in this form and we’ll be in touch.

Author