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Results for “Expert Systems”

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PubMed1993

Computer-assisted diagnosis of postmenopausal osteoporosis using a fuzzy expert system shell.

This paper shows how the AMDIS (Automated Medical Diagnosis with Intelligent Systems) integrated system can be employed to build a fuzzy medical expert system in the domain of postmenopausal osteoporosis. The fundamental aims of the expert system are to standardize knowledge and support physicians in the early detection of postmenopausal osteoporosis. A wide range of diagnostic situations has been considered for both categories of the disease, with judgments that range from disease is excluded to disease is definite. The salient aspects of the approach are the use of fuzzy logic as an analytic language for the representation and manipulation of knowledge and strategies and the integration of structured interview techniques and learning-by-example to address the knowledge acquisition task.

Adult↗

PubMed1993

Expert systems in orthodontic clinical diagnosis.

Computer-aided diagnosis of orthodontic anomalies is of particular interest in the screening of potential patients. General practitioners and public dental health workers would find considerable benefit in a reliable automated system capable of reflecting the clinical assessments of orthodontists. This study investigates the application of an expert system (Xpert Rule) to the assessment of treatment need using thirteen traits of malocclusion to construct a knowledge base. Decision trees were generated using a pruning option available with the expert system. The results of the study are encouraging and suggest that an expert system is able to generate a useful clinical screening device. Further modification of the knowledge base is required to provide a broader range of case types, particularly those of less common occurrence. It is also suggested that the random aggregation of cases is not the most effective means of developing a knowledge base.

Algorithms↗

PubMed1989

Colonic lesion expert system. Evaluation of sensitivity.

In a previous study, an expert system using visually assessed diagnostic clues for diagnosing colonic tissue as normal, adenoma or adenocarcinoma arrived at diagnoses agreeing with the evaluation by pathologists ("correct diagnoses") for all 49 cases of normal colon, for 49 of 50 cases of adenoma and for 48 of 49 cases of adenocarcinoma. The present study examined the robustness and sensitivity of the expert system to changes in the knowledge base, to changes in criteria specified by the user and to missing information. Alternative rules for combining certainty factors are discussed.

Adenocarcinoma↗

PubMedDate not supplied

NMRES: an artificial intelligence expert system for quantification of cardiac metabolites from 31phosphorus nuclear magnetic resonance spectroscopy.

The application of high-resolution 31Phosphorus Nuclear Magnetic Resonance (31P NMR) Spectroscopy in biology and medicine has provided new insights into biochemical processes and also a unique assessment of metabolites. However, accurate quantification of biological NMR spectra is frequently complicated by: (a) non-Lorentzian form of peak lineshapes, (b) contamination of peak signals by neighboring peaks, (c) presence of broad resonances, (d) low signal-to-noise ratios, and (e) poorly defined sloping baselines. Our objectives were to develop an expert system that captures and formalizes 31P NMR spectroscopists' expert knowledge, and to provide a reliable, efficient, and automated system for the interpretation of biological spectra. The NMR Expert System (NMRES) was written in the C and OPS5 programming languages and implemented on a Unix-based (Ultrix) mainframe system with XWindows bit-map graphics display. Expert knowledge was acquired from NMR spectroscopists and represented as production rules in the knowledge base. A heuristic weights method was employed to determine the confidence levels of potential peaks. Statistical and numerical methods were used to facilitate processing decisions. NMR spectra obtained from studies of ischemic neonatal and immature hearts were used to assess the performance of the expert system. The expert system performed signal extraction, noise treatment, resonance assignment, intracellular pH determination, and metabolite intensity quantitation in about 10 s per 4 KB (kilobyte) spectrum. The peak identification success rate was 98.2%. Peak areas and pH estimated by the expert system compared favorably with those determined by human experts. We conclude that the expert system has provided a framework for reliable and efficient quantification of complex biological 31P NMR spectra.

Artificial Intelligence↗

PubMed1999

Detection of CBF deficits in neuropsychiatric disorders by an expert system: a 99Tcm-HMPAO brain SPET study using automated image registration.

The aims of this study were to develop an objective method for assessing rCBF deficits using a statistical image analysis protocol and to validate its effective use in clinical practice. 99Tcm-HMPAO brain SPET images were acquired for 40 normal subjects, 10 patients with Alzheimer's disease and 10 patients with depression. Automated image registration was used to standardize the size and shape of the brain structures for all subjects. The images of the first 30 normal subjects were used to construct a normal database. The CBF images of the other 10 normal subjects and the 20 patients were compared voxel by voxel with the normal database to map CBF abnormalities by statistical evaluation. The results were compared with the clinical reports of CBF images. The expert system detected all rCBF deficits reported by the nuclear physicians. Some additional areas with special information, like atrophy and bilateral asymmetry, were also identified by the expert system. We conclude that this expert system can delineate CBF deficits with sufficiently high accuracy, differentiating normal from abnormal CBF images using voxel-based comparisons. The use of an expert system improves rCBF SPET image evaluation.

Aged↗

PubMed1992

An expert system for histological typing and grading of invasive breast cancer. First set up.

This article describes the set up of a rule-based expert system for histologic typing and grading of invasive breast cancer, which is designed to be a user-friendly tool that may be helpful for teaching and to support diagnosis making. The system raises questions and offers fixed choices to the user (usually yes/no) until a histologic diagnosis can be made with reasonable probability or enough data are available to assign a grade. As to histologic typing, the expert system is able to make the following diagnoses: ductal carcinoma, lobular carcinoma, medullary carcinoma, colloidal carcinoma, tubular carcinoma, and invasive cribriform carcinoma. If the diagnosis "ductal carcinoma" is arrived, the system offers the option to assign a histologic grade to the lesion. A first evaluation of the system in 30 cases (five each of the different subtypes) with unequivocal diagnoses by two human experts showed that the system classified 29 of the tumours in the same way as the human experts. The discrepancy case was solved after adding one rule to the system. Ten cases where a discrepancy existed between the original diagnosis of a referring centre and a reviewing human expert were all classified by the expert system in the same way as the human expert. The expert system thus seems to perform well. Further plans for evaluating, modifying and expanding the system are disclosed.

Adenocarcinoma↗

PubMed1990

A simple expert system shell for microcomputer-aided radiographic diagnosis.

A simple microcomputer-based expert system shell for radiographic diagnosis is described. The system interrogates the user by means of a simple question--and-answer approach on the radiographic findings in a given case and proffers a differential diagnosis from a database. The system also allows listing of both the radiographic findings pertinent to a specific diagnosis and all diagnoses in which a particular finding or combination of findings occur. The system database can be easily updated or modified by the user. The demonstration application for the expert system shell is the plain film diagnosis of osseous dysplasia.

Expert Systems↗

PubMedDate not supplied

Improved detection and classification of arrhythmias in noise-corrupted electrocardiograms using contextual information within an expert system.

The authors are developing an expert-system electrocardiogram (ECG) arrhythmia detector (HOBBES) for automated, long-term rhythm analysis. HOBBES employs rules and procedures that emulate how human experts analyze ECGs. This paper describes methods that HOBBES employs for improving error detection and correction in processing noisy ECGs. During periods of clean data, HOBBES develops a knowledge base that describes typical beat shapes, typical interbeat intervals between beats of different types, and patterns of beat sequences that it has observed. During periods of noisy data, HOBBES applies the information learned from the clean data to reject artifact and classify beats. HOBBES was evaluated in a noise-stress test using 35 half-hour ECG records containing a mixture of supraventricular and ventricular ectopy in normal sinus rhythm. In comparison with a classical arrhythmia detector (ARISTOTLE), HOBBES increased the number of correctly classified beats and enhanced the rejection of artifact.

Arrhythmias, Cardiac↗

PubMed2006

An expert system for differential diagnosis of tall stature syndrome.

There was created medical expert system for differential diagnosis of disorders and diseases manifested by tall stature. They were selected based on the information provided by two major computer databases, LDDB [London Dysmorphology Database] and Orphanet. Clinical signs, i.e. diagnostic criteria were developed according to 7 experts: manuals and textbooks, computer databases and online resources in pediatrics and rare diseases. Linguistic terms expressing the frequency and/or probability of presence of various symptoms were matched to numerical equivalents. The data from different experts were summarized according to the expertons' method and the inference engine was based on the Bayes theorem. An interface was made up by a set of slides with questions accompanied by boxes beside and user is expected to check corresponding boxes. The program was created in Borland C++ Builder. After having processed the entered data, the expert system produces the most probable five diagnostic possibilities and ranks them in order of likelihood.

Body Height↗

PubMed1995

Improvement in user performance following development and routine use of an expert system.

Hospital-acquired (nosocomial) infections represent a significant cause of prolonged inpatient days and additional hospital charges. In many hospitals, infection control nurses manually review positive microbiology culture results to monitor the incidence and prevalence of potential nosocomial infections. We have developed an expert system called GermWatcherTM, which uses the United States Centers for Disease Control and Prevention National Nosocomial Infection Surveillance criteria to classify microbiology results as potential nosocomial infections. In February 1993, we deployed GermWatcher at a large tertiary-care teaching hospital. In July 1993, we implemented a revised version of GermWatcher. With each version, we performed an evaluation of the program by comparing its electronic classification of positive culture results to the paper-based manual classification performed by three infection control nurses and one Infectious Disease specialist (gold standard). In the present study, we focus not on changes in the performance of the expert system, but on changes in performance among the infection control nurses. We found significant improvement in agreement and accuracy in the manual classification of cultures by the infection control nurses in the second evaluation compared to the first evaluation. We attribute this improved manual performance to the development of the expert system's rule base throughout the two evaluation phases and to the use of the expert system in the nurses' daily activities.

Cross Infection↗

PubMedDate not supplied

The Anesthesia Simulator Consultant: simulation plus expert system.

The Anesthesia Simulator Consultant was designed to provide anesthesiologists the opportunity to practice the management of anesthesia-critical incidents. The program simulates the operating room environment in a graphic display on the screen of a personal computer. Physiologic models predict the patient responses and an automated record-keeping system produces a detailed summary of the case. An expert system provides interpretations of patient information, differential diagnosis, and treatment for abnormal patient conditions. The coupling of the simulator, recorder, and expert system creates a unique self-study and evaluation environment.

Anesthesiology↗

PubMed1993

Expert systems as a support for radiological diagnosis.

Given the fact that despite high expectations, expert systems have still an almost negligible impact on the practice of medicine, the present trends and methods for medical expert systems are discussed. An analysis of the diagnostic process in radiology leads to a discussion about possible computer support and on the conditions of use and acceptance of such systems.

Cost-Benefit Analysis↗

PubMed1990

[Expert systems in oncology: concepts, problems, perspectives].

The architecture and fields of application of the expert systems in oncology are described. These systems allow systematization, developing and realizing the approximate knowledge, heuristics and algorithms of the expert-oncologists by the computer systems for solution of optimization problems of diagnosis and treatment of cancer patients. Principles of the functioning of expert systems are considered through the examples of solutions of concrete problems in oncology. Intellectual possibilities of the man-machine interaction under clinical conditions are discussed. The vistas of technological development of expert systems in oncology are evaluated.

Algorithms↗

PubMed1995

[VIE-PNN: an expert system for calculating parenteral nutrition of intensive care premature and newborn infants].

Daily renewed composition of parenteral nutrition for premature and full-term newborn infants in intensive care is time consuming and prone to inherent calculation errors. We developed a knowledge based system, VIE-PNN (Vienna Expert System for Calculating Parenteral Nutrition of Neonates) for calculating the proposed composition of parenteral nutrition on the basis of the calculating algorithm used at our neonatal intensive care unit. The system needs manual input on postnatal age, body weight, serum electrolytes (or normal values if not available), amount and content of additional oral feeds, venous access (peripheral or central), total amount of fluid intake, and complications such as sepsis (reduced lipid supply) or cholestasis (reduced amino acid supply). The parenteral nutrition proposal may interactively be modified by the attending physician. There are possibilities for error detection to reduce the probability of typing or calculation errors. The system was developed to run on IBM compatible PCs and has been tested clinically. We describe the problem domain, system structure, clinical evaluation of VIE-PNN and the calculation of a standard parenteral nutrition solution from the data stored in the system's database.

Algorithms↗

PubMed1998

The validation of an orthodontic expert system rule-base for fixed appliance treatment planning.

A peer review clinical trial was undertaken to assess the appropriateness of the advice produced by an expert system designed to plan orthodontic treatment in which the pre-adjusted bracket appliance was to be used. The results showed that the expert system's treatment plans were as reliable as those produced by a group of orthodontists. Two members of the panel actually ranked the expert system's plans more highly than their own.

Adolescent↗

PubMed1990

Development and application of a simple expert system for the interpretation of the antepartum fetal heart rate tracings (version 88/2.29).

A simple expert system is developed for the interpretation of antepartum fetal heart rate tracings. The perinatal expert chose to use the phrase 'Fetal Reserve' to describe what the cardiotocogram is indirectly measuring. Our analysis program gives numerical values to each CTG such as 5, 4, 3, 2.5, 2 and 1 corresponding to the fetal reserve conditions of good, satisfactory, probably satisfactory with uncertainty, borderline, decreased, and critical respectively. This study consists of 33 normal pregnancies with normal outcome. Each patient is followed by our computerized system biweekly from the 28th to the 38th gestational week and weekly there after. The expert system's decision for 28th, 30th, 32nd, 34th, 36th, 38th, 39th and 40th gestational weeks were 3.3 +/- 1.0, 3.8 +/- 0.7, 3.8 +/- 1.0, 4.1 +/- 0.9, 4.1 +/- 0.7, 3.6 +/- 1.0, 4.2 +/- 1.0, 3.8 +/- 0.9 and 3.4 +/- 1.2, respectively. In this study, we have used confusion matrix to determine the normal, security, and danger zones according to the perinatal expert and the expert system and the discriminatory power of the system is found to be highly significant statistically (Q = 221). We also showed that the passive test (non-stress test) in normal pregnancies has demonstrated false positive results in 4.2 and 9.3% of the cases according to the evaluations of the perinatal expert and the expert system, respectively.

Expert Systems↗

PubMed1989

[Inference engines of expert systems for designing removable partial dentures].

The expert system for designing removable partial dentures has a great potentiality for clinical and educational use. The system sorts out several solutions for clinical problems with the aid of logic and data bases stored in the memory bank. The most important part of this system is the inference engine. Production system and frame system are often used as the inference engine. The purpose of this report was to compare different types of inference engine in practical situations. Following conclusion were obtained. 1) Since each type of inference engine has its own advantages and disadvantages, one has to select most suitable one for the purpose of the system. 2) One must select the inference engine type which will help potential user's understanding.

Denture Design↗

PubMed1986

BREASTCAN: an expert system for postoperative breast cancer therapy.

An expert system, named BREASTCAN and designed to assist physicians giving postoperative adjuvant chemotherapy for breast cancer, is described. The system is based on frames, each corresponding to one stage of treatment--either a decision-making stage or a therapeutic stage. The system has been designed to allow fast and easy consultation by general practitioners lacking computer knowledge.

Breast Neoplasms↗