With the advent of computers and natural language processing, it is not surprising to see that humans are trying to use computers to answer questions. By the 1960s, there were systems implemented on the two major models of question answering, IR-based and knowledge-based, to answer questions about sport statistics and scientific facts. This paper reports on the development of a knowledge-based question answering system that is aimed at providing cognitive assistance to radiologists. Our system represents the question as a semantic query to a medical knowledge base. Evidence obtained from textual and imaging data associated with the question is then combined to arrive at an answer. This question answering system has 3 stages: i) question text and answer choices processing, ii) image processing, and iii) reasoning. Currently, the system can answer differential diagnosis and patient management questions, however, we can tackle a wider variety of question types by improving our medical knowledge coverage in the future.
Rotator cuff disease impacts over 50% of the population over 60, with reports of incidence being as high as 90% within this population, causing pain and possible loss of function. The rotator cuff is composed of muscles and tendons that work in tandem to support the shoulder. Heavy use of these muscles can lead to rotator cuff tear, with the most common causes is age-related degeneration or sport injuries, both being a function of overuse. Tears ranges in severity from partial thickness tear to total rupture. Diagnostic techniques are based on physical assessment, detailed patient history, and medical imaging; primarily X-ray, MRI and ultrasonography are the chosen modalities for assessment. The final treatment technique and imaging modality; however, is chosen by the clinician is at their discretion. Ultrasound has been shown to have good accuracy for identification and measurement of full-thickness and partial-thickness rotator cuff tears. In this study, we report on the progress and improvement of our method of transduction and analysis of in situ measurement of rotator cuff biomechanics. We have improved the ability of the clinician to apply a uniform force to the underlying musculotendentious tissues while simultaneously obtaining the ultrasound image. This measurement protocol combined with region of interest (ROI) based image processing will help in developing a predictive diagnostic model for treatment of rotator cuff disease and help the clinicians choose the best treatment technique.
Mammography is one of the most important tools for the early detection of breast cancer typically through detection of characteristic masses and/or micro calcifications. Digital mammography has become commonplace in recent years. High quality mammogram images are large in size, providing high-resolution data. Estimates of the false negative rate for cancers in mammography are approximately 10%–30%. This may be due to observation error, but more frequently it is because the cancer is hidden by other dense tissue in the breast and even after retrospective review of the mammogram, cannot be seen. In this study, we report on the results of novel image processing algorithms that will enhance the images providing decision support to reading physicians. Techniques such as Butterworth high pass filtering and Gabor filters will be applied to enhance images; followed by segmentation of the region of interest (ROI). Subsequently, the textural features will be extracted from the ROI, which will be used to classify the ROIs as either masses or non-masses. Among the statistical methods most used for the characterization of textures, the co-occurrence matrix makes it possible to determine the frequency of appearance of two pixels separated by a distance, at an angle from the horizontal. This matrix contains a very large amount of information that is complex. Therefore, it is not used directly but through measurements known as indices of texture such as average, variance, energy, contrast, correlation, normalized correlation and entropy.