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At least 199 records · Page 11Linked to original sources
PubMed1993

PTH-stimulated adenylate cyclase activity and bone histomorphometry in iliac crest biopsies in the evaluation of uremic patients: a pilot study with the use of artificial intelligence.

Iliac crest biopsies of normals, uremic patients and subjects with primary hyperparathyroidism (pHPT) were investigated. It appeared that serum 1,25- and 24,25-(OH)2-D3 correlated inversely with basal adenylate cyclase (AC) activity and relative PTH-stimulated AC, respectively. Net PTH-elicited AC (dPTH-AC) activation hence reflected individual vitamin D status. The combination variable serum PTH (s-PTH) x dPTH-AC x [H+] correlated well with resorption surface (RS) in both normals, patients with pHPT or subjects with uremia, while s-PTH, dPTH-AC activity or pH as single variables were only marginally related to RS. For all subjects analyzed, osteoid volume (OV) correlated positively with serum alkaline phosphatase but negatively with serum 1,25-(OH)2-D3. OV showed no correlation with dPTH-AC, while the relationship between OV and s-PTH was strong, suggesting that PTH stimulates osteoid deposition via some signalling pathway other than cAMP. In normals, OV was inversely proportional to s-PTH, due to homologous desensitization of this signalling system. Furthermore, s-PTH was negatively correlated with urine cAMP due to homologous desensitization of the effect of PTH on the kidney 25-(OH)-D3 1 alpha-hydroxylase. This phenomenon was absent in uremic patients. Evaluation of variables by artificial intelligence showed that the prototype uremic patient exhibited serum creatinine > 900 microM, RS > 0.12, pH between 7.15 and 7.34 and s-PTH x dPTH-AC x [H+] between 0.5 and 3.7 units with the distinguishability index 'very good' (< 5% overlap) towards normals. Average similarity of uremic patients with the prototype for normal subjects was only 22%. Cluster analysis of all the variables was conducted for comparison and yielded less clinically relevant information. Hence, emulation done by the expert system was superior and clearly indicates that present treatment modalities restore normal bone turnover only to a minor degree or not at all.

Adenylyl Cyclases↗
PubMed2005

A review on the integration of artificial intelligence into coastal modeling.

With the development of computing technology, mechanistic models are often employed to simulate processes in coastal environments. However, these predictive tools are inevitably highly specialized, involving certain assumptions and/or limitations, and can be manipulated only by experienced engineers who have a thorough understanding of the underlying theories. This results in significant constraints on their manipulation as well as large gaps in understanding and expectations between the developers and practitioners of a model. The recent advancements in artificial intelligence (AI) technologies are making it possible to integrate machine learning capabilities into numerical modeling systems in order to bridge the gaps and lessen the demands on human experts. The objective of this paper is to review the state-of-the-art in the integration of different AI technologies into coastal modeling. The algorithms and methods studied include knowledge-based systems, genetic algorithms, artificial neural networks, and fuzzy inference systems. More focus is given to knowledge-based systems, which have apparent advantages over the others in allowing more transparent transfers of knowledge in the use of models and in furnishing the intelligent manipulation of calibration parameters. Of course, the other AI methods also have their individual contributions towards accurate and reliable predictions of coastal processes. The integrated model might be very powerful, since the advantages of each technique can be combined.

Artificial Intelligence↗
PubMedDate not supplied

Artificial intelligence and Bayesian decision theory in the prediction of chemical carcinogens.

Two procedures for predicting the carcinogenicity of chemicals are described. One of these (CASE) is a self-learning artificial intelligence system that automatically recognizes activating and/or deactivating structural subunits of candidate chemicals and uses this to determine the probability that the test chemical is or is not a carcinogen. If the chemical is predicted to be carcinogen, CASE also projects its probable potency. The second procedure (CPBS) uses Bayesian decision theory to predict the potential carcinogenicity of chemicals based upon the results of batteries of short-term assays. CPBS is useful even if the test results are mixed (i.e. both positive and negative responses are obtained in different genotoxic assays). CPBS can also be used to identify highly predictive as well as cost-effective batteries of assays. For illustrative purposes the ability of CASE and CPBS to predict the carcinogenicity of a carcinogenic and a non-carcinogenic polycyclic aromatic hydrocarbon is shown. The potential for using the two methods in tandem to increase reliability and decrease cost is presented.

Animals↗
PubMed1993

Rule based artificial intelligence expert system for determination of upper extremity impairment rating.

Quantitative evaluation of upper extremity impairment, a percentage rating most often determined using a rule based procedure, has been implemented on a personal computer using an artificial intelligence, rule-based expert system (AI system). In this study, the rules given in Chapter 3 of the AMA Guides to the Evaluation of Permanent Impairment (Third Edition) were used to develop such an AI system for the Apple Macintosh. The program applies the rules from the Guides in a consistent and systematic fashion. It is faster and less error-prone than the manual method, and the results have a higher degree of precision, since intermediate values are not truncated.

Arm Injuries↗
PubMed2026

Artificial Intelligence for Diagnosing Meibomian Gland Dysfunction: A Systematic Review and Meta-Analysis of Diagnostic Test Accuracy Studies.

PURPOSE: To identify, appraise, and synthesize the performance of artificial intelligence-based meibography reading as compared with human graders in diagnosing meibomian gland dysfunction. METHODS: We followed Cochrane methodology and reporting guidelines for diagnostic test accuracy reviews. To assess potential risk of bias and applicability, we used a modified Quality Assessment of Diagnostic Accuracy Studies-2 checklist. We applied bivariate logistic models to estimate summary sensitivity and specificity when appropriate and used the GRADE framework to rate the certainty of the evidence. RESULTS: We identified 14 eligible studies involving 5511 predominantly middle-aged participants (average age: 27-55 years) who were primarily female (&#x2265;54.5%). A total of 18,926 meibography images were obtained through noncontact infrared (11 studies) or in vivo confocal microscopy (three studies). Two studies reported external validation of deep learning models, 12 reported internally validated models, and one reported both. All but one study had high risk of bias in at least one domain; 12 studies raised high or intermediate concern about applicability. Based on three external evaluations, the summary sensitivity and specificity for diagnosing meibomian gland dysfunction from normal glands were 97.5% (95% confidence interval: 77.5%-99.8%) and 85.5% (95% confidence interval: 47.3%-97.5%). Sources of heterogeneity in internally validated models included study population, case mix, and others. The overall evidence was very low to low certainty because of imprecision, high risk of bias, and concerns about applicability. CONCLUSIONS: Artificial intelligence-based meibography grading appears less accurate than human graders. Future studies should adopt rigorous designs, including a more diverse participant pool (or image set), and external validation.

Humans↗
PubMed1989

Artificial intelligence in the prediction of operative findings in low back surgery.

In a prospective trial of 150 patients undergoing first time low back surgery for sciatica, the performance of a computer was compared to that of clinicians in predicting the likely operative findings. The results indicate that artificial intelligence techniques implemented on a computer can be used for predicting operative findings in low back surgery, can out-perform clinicians and can be used to develop better methods of human prediction.

Adult↗
PubMed1988

Artificial intelligence in the diagnosis of low-back pain and sciatica.

In a prospective trial of 200 patients with low-back pain or sciatica, the diagnostic performance of a computer was compared with that of a clinician in a variety of clinical settings. The results indicate that artificial intelligence techniques can be used for the differential diagnosis of low-back disorders, can outperform clinicians, and can be used to develop better methods of human differential diagnosis.

Back Pain↗
PubMed1988

Artificial intelligence: a computerized decision aid for trauma.

A computerized decision support system has been developed to advise ATLS-trained surgeons on the initial definitive management of patients with penetrating injuries of the abdomen immediately following resuscitation and stabilization. The program was developed as an "expert system," using the techniques of artificial intelligence. It is able to suggest: the need for further examination; additional tests; diagnoses; and treatments. In this study, the advice offered by the expert system was compared to that of physicians-in-training. Five actual patient care situations were presented to the system and to 13 medical students and surgical residents: four MS-III, three PGY-I, three PGY-III, and three PGY-V. The suggestions of each of the 13 trainees, the advice of the expert system, and the actual management were blinded. Five surgeons versed in trauma and otherwise not involved in the project judged whether each of the 15 purported management plans was acceptable and ranked them in order of preference. Only the actual care and the advice from the system were judged acceptable for all five problems. The rankings of the expert system were better than those of any individual trainee. The differences were statistically significant for two of the three chief residents, five of nine residents overall, and all four students. This preliminary validation of a prototype expert system is encouraging for the prospect of a computerized decision support system that can help surgeons make initial definitive management plans for patients with major trauma.

Abdominal Injuries↗
PubMed2006

Robotics and artificial intelligence: Jewish ethical perspectives.

In 16th Century Prague, Rabbi Loew created a Golem, a humanoid made of clay, to protect his community. When the Golem became too dangerous to his surroundings, he was dismantled. This Jewish theme illustrates some of the guiding principles in its approach to the moral dilemmas inherent in future technologies, such as artificial intelligence and robotics. Man is viewed as having received the power to improve upon creation and develop technologies to achieve them, with the proviso that appropriate safeguards are taken. Ethically, not-harming is viewed as taking precedence over promoting good. Jewish ethical thinking approaches these novel technological possibilities with a cautious optimism that mankind will derive their benefits without coming to harm.

Biomedical Technology↗
PubMed1992

Artificial intelligence in automated classification of rat vaginal smear cells.

Microscopic examination of vaginal smears has been used routinely to determine the stage of the estrous cycle of female rats in reproductive research. The stage of the estrous cycle is based on relative counts of nucleated epithelial cells, cornified epithelial cells and leukocytes. The purpose of this project was to explore automation of vaginal smear analysis using image processing and artificial intelligence techniques. A fully connected back-propagation neural network was used to locate all potential objects in a digitized scene. A unique algorithm was then employed to center a subsequent sampling box to collect pixel intensity values from the red and green components of each image. A final neural network was used in the classification of cell type. Neural networks were used because of their ability to generalize among input patterns and to tolerate extraneous noise due to variations in staining artifacts and aberrant illumination of the microscope field. This preliminary cell diagnosing system not only provides the basis for the fully automated system but also provides a method by which many other cytologic image processing problems can be automated.

Animals↗
PubMedDate not supplied

Reasoning methods in medical consultation systems: artificial intelligence approaches.

It has been argued that the problem of medical diagnosis is fundamentally ill-structured, particularly during the early stages when the number of possible explanations for presenting complaints can be immense. This paper discusses the process of clinical hypothesis evocation, contrasts it with the structured decision making approaches used in traditional computer-based diagnostic systems, and briefly surveys the more open-ended reasoning methods that have been used in medical artificial intelligence (AI) programs. The additional complexity introduced when an advice system is designed to suggest management instead of (or in addition to) diagnosis is also emphasized. Example systems are discussed to illustrate the key concepts.

Diagnosis, Computer-Assisted↗