Pectus excavatum is among the most common major congenital anomalies of the chest wall whose correction can be performed via minimally invasive Nuss technique that places a pectus bar to elevate the sternum anteriorly. However, the size and bending of the pectus bar are manually modeled intraoperatively by trial-and-error. The procedure requires intense pain management in the months following surgery. In response, we are developing a novel distraction device for incremental and personalized PE correction with minimal risk and pain, akin to orthodontic treatment using dental braces. To design the device, we propose in this study a personalized surgical planning framework for PE correction from clinical noncontrast CT. First, we segment the ribs and sternum via kernel graph cuts. Then costal cartilages, which have very low contrast in noncontrast CT, are modeled as 3D anatomical curves using the cosine series representation and estimated using a statistical shape model. The size and shape of the correction device are estimated through model fitting. Finally, the corrected/post-surgical chest is simulated in relation to the estimated shape of correction device. The root mean square mesh distance between the estimated cartilages and ground truth on 30 noncontrast CT scans was 1.28 +/- 0.81 mm. Our method found that the average deformation of the sterna and cartilages with the simulation of PE correction was 49.71 +/- 10.11 mm.
We present a technique for the computational analysis of craniosynostosis from CT images. Our fully automatic methodology uses a statistical shape model to produce diagnostic features tailored to the anatomy of the subject. We propose a computational anatomy approach for measuring shape abnormality in terms of the closest case from a multi-atlas of normal cases. Although other authors have tackled malformation characterization for craniosynostosis in the past, our approach involves several novel contributions (automatic labeling of cranial regions via graph cuts, identification of the closest morphology to a subject using a multi-atlas of normal anatomy, detection of suture fusion, registration using masked regions and diagnosis via classification using quantitative measures of local shape and malformation). Using our automatic technique we obtained for each subject an index of cranial suture fusion, and deformation and curvature discrepancy averages across five cranial bones and six suture regions. Significant differences between normal and craniosynostotic cases were obtained using these characteristics. Machine learning achieved a 92.7% sensitivity and 98.9% specificity for diagnosing craniosynostosis automatically, values comparable to those achieved by trained radiologists. The probability of correctly classifying a new subject is 95.7%.
Adrenal abnormalities are commonly identified on computed tomography (CT) and are seen in at least 5 % of CT examinations of the thorax and abdomen. Previous studies have suggested that evaluation of Hounsfield units within a region of interest or a histogram analysis of a region of interest can be used to determine the likelihood that an adrenal gland is abnormal. However, the selection of a region of interest can be arbitrary and operator dependent. We hypothesize that segmenting the entire adrenal gland automatically without any human intervention and then performing a histogram analysis can accurately detect adrenal abnormality. We use the random forest classification framework to automatically perform a pixel-wise classification of an entire CT volume (abdomen and pelvis) into three classes namely right adrenal, left adrenal, and background. Once we obtain this classification, we perform histogram analysis to detect adrenal abnormality. The combination of these methods resulted in a sensitivity and specificity of 80 and 90 %, respectively, when analyzing 20 adrenal glands seen on volumetric CT datasets for abnormality.
Radiologists, referring physicians, and patients all have certain legal rights regarding access to medical records, including imaging data. The degree of patient access is likely to increase with the growing adoption of patient portals and personal health records. In addition, referring physicians and radiologists have a collective responsibility to ensure that important findings are transferred appropriately between their practices. In some cases when this is not possible, communicating directly with patients is the best way to protect the interests of both patients and radiologists. Even when not required, some radiologists have extensive experience communicating results directly to patients. Direct communication of radiology results to patients may present an opportunity to satisfy patients and reassert the importance of the physician-patient relationship in radiology.
Both outcomes researchers and informaticians are concerned with information and data. As such, some of the central challenges to conducting successful comparative effectiveness research can be addressed with informatics solutions.
The purpose of this study was to evaluate the diagnostic accuracy of radiologists using monochrome medical-grade 5 megapixel (MP), 3 MP, 2 MP, and 1 MP displays for the detection of cervical fractures on cervical radiographs, while controlling factors such as luminance and ambient conditions.
Knee-related injuries including meniscal tears are common in both young athletes and the aging population, and require accurate diagnosis and surgical intervention when appropriate. With proper techniques and radiologists experienced skills, confidence in detection of meniscal tears can be quite high. This paper develops a novel computer-aided detection (CAD) diagnostic system for automatic detection of meniscal tears in the knee. Evaluation of this CAD system using an archived database of images from 40 individuals with suspected knee injuries indicates that the sensitivity and specificity of the proposed CAD system are 83.87% and 75.19%, respectively, compared to the mean sensitivity and specificity of 77.41% and 81.39%, respectively, obtained by experienced radiologists in routine diagnosis without using the CAD. The experimental results suggest that the developed CAD system has great potential and promise in automatic detection of both simple and complex meniscal tears of the knee.
Clinical and contextual information associated with images may influence how radiologists draw diagnostic inferences, highlighting the need to control multiple sources of bias in the methodologic design of investigations involving radiologic interpretation. In the past, manual control methods to mask review films presented in practice have been used to reduce potential interpretive bias associated with differences between viewing images for patient care and reviewing images for the purposes of research, education, and quality improvement. These manual precedents from the film era raise the question whether similar methods to reduce bias can be implemented in the modern digital environment.
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JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.
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