PubMed · 15348999
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Robert L Tomsak. 2004. Projector light visual fields.. https://doi.org/10.1097/00041327-200409000-00025
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Endometrial intraepithelial neoplasia (also known as 'EIN') is a precursor to endometrioid endometrial adenocarcinoma characterized by monoclonal growth of mutated cells, a distinctive histopathologic appearance, and 45-fold elevated cancer risk. We have applied diagnostic criteria for EIN to 97 successive endometrial biopsies classified as hyperplastic according to World Health Organization criteria and correlated results with computer-assisted morphometry (D-score) and clinical cancer outcomes. Three pathologists separately reviewed all cases for presence or absence of EIN using published criteria (gland area>stromal area, cytologic change in focus of altered architecture, lesion size>1 mm, and exclusion of cancer and mimics). Discordant cases were resolved by a consensus review at a multiheaded scope. Clinical outcomes were obtained in 84 patients from patient visit and pathology records. Diagnoses of presence or absence of EIN were unanimous among all three pathologists in 75% of cases, and intraobserver-reproducibility was very good (kappa 0.73-0.90). Cases rediagnosed as EIN encompassed hyperplasias previously diagnosed as atypical (n=18) or nonatypical (eight complex, two simple). Eight follow-up cancers were scattered between hyperplasia types (5/21 atypical, 3/63 nonatypical), but all classified as EIN (8/25) and D-score <or=1 (8/38). Subjective application of criteria for diagnosis of EIN correlates well with objective morphometry and successfully segregates patients into high and low cancer risk subgroups with better reproducibility than atypical hyperplasia diagnosis.
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.