Computer study of thyroid diseases. III. Statistical models.

S Ever-Hadani; N B Grover; A Gordon

Home/Archive/PubMed

PubMed · 328451

Computer study of thyroid diseases. III. Statistical models.

S Ever-Hadani·N B Grover·A Gordon

View original source ↗Export metadata ↓

Abstract

A binary-variable multiple-regression model is proposed for the computer diagnosis of thyroid disease. The procedure employs a multiple-regression technique in which each variable is assigned a value of either unity or zero. Unlike other current approaches, the proposed model uses only information about the patients themselves; no prior knowledge of the incidence of the disease in the population under study is required. The model was applied to data on 906 patients with suspected thyroid disorders referred to endocrine clinics in Israel. On the basis of simple physical findings and clinical signs and symptoms, the correct diagnosis was obtained in 95% of the cases for males and in 85% for females.

Explore related subjects

Diagnosis, Computer-Assisted Humans Models, Theoretical Regression Analysis Thyroid Diseases Journal Article

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

S Ever-Hadani, N B Grover, A Gordon. 1977. Computer study of thyroid diseases. III. Statistical models.. https://pubmed.ncbi.nlm.nih.gov/328451/

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2007

[Use of a screening device for sleep apnea in clinical practice].

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.

Diagnosis, Computer-Assisted↗

PubMed2007

Computer-aided diagnosis for improved detection of lung nodules by use of posterior-anterior and lateral chest radiographs.

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.

Diagnosis, Computer-Assisted↗

PubMed2006

Improvement of the detection of myocardial ischemia thanks to information technologies.

BACKGROUND: The standard 12-lead ECG remains one of the basic investigations for the early detection and assessment of acute coronary syndromes. It is easy to perform, anywhere and anytime, and can be digitally transmitted within minutes to an emergency medical service for remote advice and triage. But the conventional ST-segment deviation criteria are of limited diagnostic accuracy. The purpose of this study is to investigate how much the use of computerized ECG techniques based on the measurement of the serial spatiotemporal ECG changes could improve the detection accuracy of transmural myocardial ischemia. METHODS: We considered the serial changes of continuous 12-lead ECGs of 90 patients undergoing elective percutaneous coronary angioplasty (PTCA) recorded during balloon inflation as an experimental model of ECG changes induced by coronary artery occlusion. The spatiotemporal ECG changes were measured according to the CAVIAR method and assessed by multivariate discriminant analysis in reference to serial changes of control recordings and standard ECG criteria. RESULTS: The diagnostic accuracy of the CAVIAR criteria for ischemia detection was 97%, with sensitivity of 98% and specificity of 96%, whereas the diagnostic accuracy of the conventional ST-segment criteria was 74%, with sensitivity of 60% and specificity of 88%. The increase of overall performance was obtained for all the occlusion locations. CONCLUSIONS: Computer-assisted quantitative serial ECG analysis, taking into account the spatiotemporal changes of the QRS and T waves, would provide the physician with additional information for significantly improving the detection of transmural myocardial ischemia.

Diagnosis, Computer-Assisted↗