An effort to capture anomalies from medical imaging, package those with other data, and deliver it for use by clinicians.
If you think of each medical image as represented a large amount of data, the underlying idea is to filter out all but the most relevant data, so that clinicians are not confronting an overload of information.
In network terms, rather than displaying all of the current connections to a network (the ever popular eye-candy view of connections), displaying only those connections that are different from all the rest.
The same technique could be usefully applied in a number of “big data” areas.
From the post:
Medical Sieve is an ambitious long-term exploratory grand challenge project to build a next generation cognitive assistant with advanced multimodal analytics, clinical knowledge and reasoning capabilities that is qualified to assist in clinical decision making in radiology and cardiology. It will exhibit a deep understanding of diseases and their interpretation in multiple modalities (X-ray, Ultrasound, CT, MRI, PET, Clinical text) covering various radiology and cardiology specialties. The project aims at producing a sieve that filters essential clinical and diagnostic imaging information to form anomaly-driven summaries and recommendations that tremendously reduce the viewing load of clinicians without negatively impacting diagnosis.
Statistics show that eye fatigue is a common problem with radiologists as they visually examine a large number of images per day. An emergency room radiologist may look at as many 200 cases a day, and some of these imaging studies, particulary lower body CT angiography can be as many as 3000 images per study. Due to the volume overload, and limited amount of clinical information available as part of imaging studies, diagnosis errors, particularly relating to conincidental diagnosis cases can occur. With radiologists also being a scarce resource in many countries, it will even more important to reduce the volume of data to be seen by clinicians particularly, when they have to be sent over low bandwidth teleradiology networks.
MedicalSieve is an image-guided informatics system that acts as a medical sieve filtering the essential clinical information physicians need to know about the patient for diagnosis and treatment planning. The system gathers clinical data about the patient from a variety of enterprise systems in hospitals including EMR, pharmacy, labs, ADT, and radiology/cardiology PACs systems using HL7 and DICOM adapters. It then uses sophisticated medical text and image processing, pattern recognition and machine learning techniques guided by advanced clinical knowledge to process clinical data about the patient to extract meaningful summaries indicating the anomalies. Finally, it creates advanced summaries of imaging studies capturing the salient anomalies detected in various viewpoints.
Medical Sieve is leading the way in diagnostic interpretation of medical imaging datasets guided by clinical knowledge with many first-time inventions including (a) the first fully automatic spatio-temporal coronary stenosis detection and localization from 2D X-ray angiography studies, (b) novel methods for highly accurate benign/malignant discrimination in breast imaging, and (c) first automated production of AHA guideline17 segment model for cardiac MRI diagnosis.
For more details on the project, please contact Tanveer Syeda-Mahmood (>stf@us.ibm.com).
You can watch a demo of our Medical Sieve Cognitive Assistant Application here.
Curious: How would you specify the exclusions of information? So that you could replicate the “filtered” view of the data?
Replication is a major issue in publicly funded research these days. Not reason for that to be any different for data science.
Yes?