PubMed · 4583087
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N Victor. 1973. [Probabilistic allocation rules (author's transl)].. https://pubmed.ncbi.nlm.nih.gov/4583087/
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The purpose was to introduce a novel method for computer-based classification of visual field data derived from perimetric examination, that may act as a 'counsellor', providing an independent 'second opinion' to the diagnosing physician. The classification system consists of a Hopfield-type neural attractor network that obtains its input data from perimetric examination results. An iterative relaxation process determines the states of the neurons dynamically. Therefore, even 'noisy' perimetric output, e.g., early stages of a disease, may eventually be classified correctly according to the predefined idealized visual field defect (scotoma) patterns, stored as attractors of the network, that are found with diseases of the eye, optic nerve and the central nervous system. Preliminary tests of the classification system on real visual field data derived from perimetric examinations have shown a classification success of over 80%. Some of the main advantages of the Hopfield-attractor-network-based approach over feed-forward type neural networks are: (1) network architecture is defined by the classification problem; (2) no training is required to determine the neural coupling strengths; (3) assignment of an auto-diagnosis confidence level is possible by means of an overlap parameter and the Hamming distance. In conclusion, the novel method for computer-based classification of visual field data, presented here, furnishes a valuable first overview and an independent 'second opinion' in judging perimetric examination results, pointing towards a final diagnosis by a physician. It should not be considered a substitute for the diagnosing physician. Thanks to the worldwide accessibility of the Internet, the classification system offers a promising perspective towards modern computer-assisted diagnosis in both medicine and tele-medicine, for example and in particular, with respect to non-ophthalmic clinics or in communities where perimetric expertise is not readily available.
BACKGROUND: Early prediction of infection following surgery for gastric cancer may permit earlier intervention. AIM: The aim was to develop an early diagnostic system for postoperative infection. METHODS: Clinical and laboratory data were analyzed in 180 patients who had surgery for gastric cancer at the Wakayama Medical University Hospital (January 1992 to December 1994). Of these, 60 patients developed a postoperative infection. A predictive system was then devised using fuzzy theory and evaluated in a second set of 137 patients who underwent surgery for gastric cancer at Wakayama Medical University and 10 other associated hospitals (August 1995 to January 1997). RESULTS: The system identified seven parameters grouped into six rules and entered into a fuzzy logic system to predict either infection or non-infection. (1) Blood loss, and extent of resection; (2) the febrile pattern during days 2 to 4; (3) differential leukocyte count on day 4; (4) C-reactive protein level on day 4; (5) time sequential vectors during days 1-4 for the band cell neutrophil count, and (6) leukocyte count. The sensitivity was 86% (24/28), the specificity was 90% (98/109), the negative predictive value was 96% (98/102), the positive predictive value was 69% (24/35) and the overall accuracy was 80% (122/137). CONCLUSION: Fuzzy theory can provide early prediction of postoperative infection using standard clinical and biochemical parameters. The clinical utility of this system needs to be determined in future studies.
BACKGROUND: Differential diagnosis of melanoma from melanocytic nevi is often not straightforward. Thus, a growing interest has developed in the last decade in the automated analysis of digitized images obtained by epiluminescence microscopy techniques to assist clinicians in differentiating early melanoma from benign skin lesions. PURPOSE: The aim of this study was to evaluate diagnostic accuracy provided by different statistical classifiers on a large set of pigmented skin lesions grabbed by four digital analyzers located in two different dermatological units. EXPERIMENTAL DESIGN: Images of 391 melanomas and 449 melanocytic nevi were included in the study. A linear classifier was built by using the method of receiver operating characteristic curves to identify a threshold value for a fixed sensitivity of 95%. A K-nearest-neighbor classifier, a nonparametric method of pattern recognition, was constructed using all available image features and trained for a sensitivity of 98% on a large exemplar set of lesions. RESULTS: On independent test sets of lesions, the linear classifier and the K-nearest-neighbor classifier produced a mean sensitivity of 95% and 98% and a mean specificity of 78% and of 79%, respectively. CONCLUSIONS: In conclusion, our study suggests that computer-aided differentiation of melanoma from benign pigmented lesions obtained with DB-Mips is feasible and, above all, reliable. In fact, the same instrumentations used in different units provided similar diagnostic accuracy. Whether this would improve early diagnosis of melanoma and/or reducing unnecessary surgery needs to be demonstrated by a randomized clinical trial.