PubMed · 4622247
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1972. [A computer analyzes poisons].. https://pubmed.ncbi.nlm.nih.gov/4622247/
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About 2-4% of adults suffer from obstructive sleep apnea (OSA), which is the most common sleep-related breathing disorder (SRBD). Repetitive obstructions of the upper airway mean that it is associated with hypertension and an elevated cardio- and cerebrovascular morbidity, which can be lowered by means of effective therapy. These patients cannot be treated correctly unless they are identified early in the course of the condition. Therefore, this study evaluated the use of the microMESAM screening device (known up to December 2005 as the "ApneaLink") in 61 patients in whom SRBD was suspected. The device proved reliable and practical in application.
RATIONALE AND OBJECTIVES: We developed a computerized scheme for detection of lung nodules in the lateral views of chest radiographs, in order to improve the overall performance in combination with the computer-aided diagnostic (CAD) scheme for posterior-anterior (PA) views. MATERIALS AND METHODS: We used 106 pairs of PA and lateral views of chest radiographs (122 lung nodules) for development of the CAD scheme. In the CAD scheme for lateral views, initial candidates of lung nodules were identified by use of a nodule enhancement filter based on the edge gradients. Thirty-four image features extracted from the original and the nodule-enhanced images were used for the rule-based scheme and for artificial neural networks (ANNs) for removal of some false-positive candidates. The computer performance was evaluated with a leave-one-case-out test method for ANNs. For PA views, we used the existing CAD scheme, which was trained with one-half of 924 chest images and then tested with the remaining images. RESULTS: When the CAD scheme was applied only to PA views, the sensitivity in the detection of lung nodules was 70.5%, with 4.9 false positives per image. Although the performance of the computerized scheme for lateral views was relatively low (60.7% sensitivity with 1.7 false positives per image), the overall sensitivity (86.9%) was improved (6.6 false positives per two views), because 20 (16.4%) of the 122 nodules were detected only on lateral views. CONCLUSIONS: The CAD scheme by use of lateral-view images has the potential to improve the overall performance for detection of lung nodules on chest radiographs when combined with a conventional CAD scheme for standard PA views.
In this paper, to utilize the third dimension of Computed Tomography, regions of interest (ROI) slices were combined to form 3D ROI image and a 3D template was determined to find the structures with similar properties of nodules. Convolution of 3D ROI image with the proposed template strengthens the shapes similar to the template and weakens the other ones. False-positive (FP) per nodule and per slice versus diagnosis sensitivity were obtained. The Computer Aided Diagnosis system achieved 100% sensitivity with 0.83 FP per nodule and 0.46 FP per slice, when the nodule thickness was greater than or equal to 5.625 mm.