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
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.
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