Pulmonary Nodule Detection by a Team of Medical Practitioners
Published date: 12.02.2021
Read time: 1 min
Client Profile
Industry: Medical Software
Location: Israel
Size: 50-100 employees
Company Bio
Our client provides artificial intelligence software for full body imaging. With the help of deep learning and AI algorithms, this software analyzes medical images and patient data to help radiologists detect critical anomalies.
Overview
To create advanced healthcare-grade AI-based decision support software, massive amounts of unsorted and encrypted lung scans needed to be annotated. These scans had to be sorted, grouped, and checked for pulmonary nodules. Pulmonary nodules then had to be marked as prominent, non-prominent, borderline, or granuloma.
Challenge
Our main challenge was that no specific instructions nor any annotation tools were provided to our team. The output format was not standard, so the conversion took more time and effort than anticipated.
Human Resources
Mindy Support worked with three radiologists each who had 5+ years of experience and were also members of the Radiologist’s Association of Ukraine, and one was also a member of the European Society of Radiology. A dedicated project manager was also provided.
Solution Process
Results
Our dedicated team of 3 radiologists and 1 project manager delivered the following results:
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We have a minimum threshold for starting any new project, which is 735 productive man-hours a month (equivalent to 5 graphic annotators working on the task monthly).
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