Inflammatory joint disease: a comparison of liposome scanning, bone scanning, and radiography.

Patients with rheumatoid arthritis, psoriatic arthritis, and osteoarthritis were assessed by clinical evaluation, radiography, and joint scintigraphy using technetium labelled methylene diphosphonate (MDP) and technetium labelled liposomes. Although both scanning techniques were more sensitive than radiographs in detecting joint disease, the liposomes scans were positive only in clinically active inflammatory disease. In patients with rheumatoid arthritis liposome scintigraphy was also able to discriminate between different grades of joint tenderness. In inactive inflammatory polyarthropathies, although the MDP bone scans continued to show increased activity, the liposome scans did not and were therefore a more accurate reflection of the clinical state. The increased uptake in the liposome scans may be due to incorporation of the liposomes into the phagocytic cells of the synovium. This scan may, therefore, by reflecting the activity of cells involved in the disease process, provide a useful way of assessing disease activity and progression.     

A study to determine what variables predict institutionalization of elderly people

The purpose of this study was to determine which variables distinguish newly institutionalized elderly people from elderly people able to remain in the community. The findings suggest that old age and lack of a social support network are major predictors of institutionalization rather than health and social variables.     

Computer aided diagnosis of breast cancer on mammograms

Computer-aided diagnosis (CAD) is a diagnosis made by a physician who takes into account the computer output of quantitative analysis of mammograms. CAD schemes in mammography have been developed to detect lesions such as clustered microcalcifications and masses, and also to distinguish between benign and malignant lesions. Computerized schemes are composed of three major steps which are image processing, quantitation of image features, and data classification. The current performance level of detecting clustered microcalcifications by computer is approximately 85% at a false positive rate of 0.5 per mammogram, whereas the detection accuracy of masses is approximately 90% at a false positive rate of 2 per mammogram. Observer performance studies indicated that computer output can improve the performance of radiologists in detecting clustered microcalcifications by increasing the dtection accuracy to 90% from 80% at a specificity of 90%. The automated classification of clustered microcalcifications is based on quantitative analysis of image features of individual microcalcifications and cluster, followed by artificial neural networks (ANNs) for data classification. With our database, the computer scheme correctly identified 82% of patients with benign lesions, all of whom had biopsies (ie, the radiologist thought the microcalcifications were suspicious for malignancy), and 100% of patients with malignant lesions. On the same set of images, the average of five radiologists was only 27% correct in classifying lesions as benign at 100% sensitivity. The automated classification of masses is made by the quantitation of image features of masses together with a rule-based and ANNs method for data classification. The computer scheme achieved, at 100% sensitivity, a positive predictive value of 83%, which was 12% higher than that of the experienced mammographer and 21% higher than that of the average of less experienced mammographers. The first prototype intelligent workstation for mammography was developed at the University of Chicago, and applied to approximately 12000 screening cases for the detection of early breast cancers. Promising initial results were obtained with the workstation.     

Volumetric texture analysis of breast lesions on contrast-enhanced magnetic resonance images.

Automated image analysis aims to extract relevant information from contrast-enhanced magnetic resonance images (CE-MRI) of the breast and improve the accuracy and consistency of image interpretation. In this work, we extend the traditional 2D gray-level co-occurrence matrix (GLCM) method to investigate a volumetric texture analysis approach and apply it for the characterization of breast MR lesions. Our database of breast MR images was obtained using a T1-weighted 3D spoiled gradient echo sequence and consists of 121 biopsy-proven lesions (77 malignant and 44 benign). A fuzzy c-means clustering (FCM) based method is employed to automatically segment 3D breast lesions on CE-MR images. For each 3D lesion, a nondirectional GLCM is then computed on the first postcontrast frame by summing 13 directional GLCMs. Texture features are extracted from the nondirectional GLCMs and the performance of each texture feature in the task of distinguishing between malignant and benign breast lesions is assessed by receiver operating characteristics (ROC) analysis. Our results show that the classification performance of volumetric texture features is significantly better than that based on 2D analysis. Our investigations of the effects of various of parameters on the diagnostic accuracy provided means for the optimal use of the approach.     

Assessing Interdisciplinary Trainees’ Objective and Self-Reported Knowledge of Autism Spectrum Disorder and Confidence in Providing Services

Journal of Autism and Developmental Disorders - The importance of accurate identification and high-quality intervention for individuals with autism spectrum disorder (ASD) is indisputable....     

Brief Report: A Brief Video Intervention for Increasing Autism Knowledge in a General Population Sample

Journal of Autism and Developmental Disorders - As many individuals in the general population will likely interact with autistic persons in various contexts, ensuring adequate autism knowledge and...     

Evaluation of chemical dot thermometers for measuring body temperature of orally intubated patients.

BACKGROUND: Recent research indicates that oral measurement of body temperature is a reliable option in orally intubated patients. In situations such as protective isolation, where dedicated electronic thermometers are not available, are single-use chemical dot thermometers an acceptable alternative? OBJECTIVE: To determine the accuracy of single-use chemical dot thermometers in orally intubated adult patients. METHODS: Subjects included a convenience sample of 85 adult patients admitted to 1 of 2 intensive care units (surgical trauma and neuroscience). For each patient, oral temperatures were measured concurrently (within 5 minutes) with a chemical dot thermometer and an electronic thermometer. The sequence of temperature measurements was alternated with each subsequent patient. Both thermometers were placed in the same posterior sublingual pocket opposite the side of the endotracheal tube. RESULTS: Measurements obtained with electronic and single-use chemical dot thermometers correlated strongly (r = 0.937). With the chemical dot thermometer, body temperature was overestimated in 11.8% of the measurements and underestimated in 10.8% of the measurements by 0.4 degree C or more. The difference between oral temperatures measured with the 2 different thermometers was not related to the patient's age, sex, or sublingual pocket location or to the order of thermometer use. CONCLUSION: The chemical dot thermometer is useful and reliable for measuring body temperature of orally intubated patients. When measurements of body temperature have important consequences for decisions about treatment, clinicians should use an electronic thermometer to confirm measurements made with a chemical dot thermometer.