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PubMed1997

Melanie II--a third-generation software package for analysis of two-dimensional electrophoresis images: II. Algorithms.

After two generations of software systems for the analysis of two-dimensional electrophoresis (2-DE) images, a third generation of such software packages has recently emerged that combines state-of-the-art graphical user interfaces with comprehensive spot data analysis capabilities. A key characteristic common to most of these software packages is that many of their tools are implementations of algorithms that resulted from research areas such as image processing, vision, artificial intelligence or machine learning. This article presents the main algorithms implemented in the Melanie II 2-D PAGE software package. The applications of these algorithms, embodied as the feature of the program, are explained in an accompanying article (R. D. Appel et al.; Electrophoresis 1997, 18, 2724-2734).

Algorithms↗

PubMedDate not supplied

Expert systems in medicine: academic exercise or practical tool?

From the earliest stages of computer development, scientists have aspired to the creation of advanced systems which could simulate human thought and reasoning. Of all the modern technological quests, this research to create artificially intelligent computer systems has been one of the most ambitious and fascinating. Although attempts have been made for the last 30 years to develop and apply such systems to the medical sciences, only limited progress has been made. This paper reviews and discusses the important role of expert systems in medicine and outlines future trends in this area.

Artificial Intelligence↗

PubMed1994

AI-based approach to automatic sleep classification.

The primary goal of this paper is to introduce the potential of artificial intelligence (AI) methods to researchers in sleep classification. AI provides learning procedures for the construction of a sleep classifier, prescribing how to combine the observed parameters and how to derive the corresponding decision thresholds. A case study reporting a successful application of an automatic induction of decision trees and of a learning vector quantizer to this domain is presented.

Artificial Intelligence↗

PubMed2000

The traveling salesman problem: a hierarchical model.

Our review of prior literature on spatial information processing in perception, attention, and memory indicates that these cognitive functions involve similar mechanisms based on a hierarchical architecture. The present study extends the application of hierarchical models to the area of problem solving. First, we report results of an experiment in which human subjects were tested on a Euclidean traveling salesman problem (TSP) with 6 to 30 cities. The subject's solutions were either optimal or near-optimal in length and were produced in a time that was, on average, a linear function of the number of cities. Next, the performance of the subjects is compared with that of five representative artificial intelligence and operations research algorithms, that produce approximate solutions for Euclidean problems. None of these algorithms was found to be an adequate psychological model. Finally, we present a new algorithm for solving the TSP, which is based on a hierarchical pyramid architecture. The performance of this new algorithm is quite similar to the performance of the subjects.

Algorithms↗

PubMed1993

Integrating knowledge-based systems and databases.

In recent years, there has been an explosion of interest among the computing community in the field of artificial intelligence, particularly in the areas of natural language processing and knowledge-based systems (KBS). The medical domain has seen the development of hundreds of KBSs and there is substantial evidence to show that the application of a knowledge-based approach to decision support can go a long way towards overcoming the information overload experienced by many clinicians today. Yet many of these medical KBSs are still at the prototype stage and are mainly confined to research laboratories. There are many reasons for this apparently slow take-up of the technology, but one of the most significant is the lack of integration into the regular routine information processing of the organisation, in particular the database processing. This paper discusses the benefits of such integration and methods for achieving it in the context of general trends in information systems. Database technology provides efficient and secure management of large amounts of data in a multi-user, multi-application environment. Knowledge-based technology, on the other hand, provides mechanisms for building intelligent systems. Thus, for example, given a set of facts about a domain (symptoms, laboratory test results, etc.) together with a set of rules which apply to that domain (e.g. 'if TT4 > 150 nmol/l then suspect hyperthyroidism'), a KBS can deduce new information about that domain automatically. The effective integration of these two technologies is seen as a means of achieving the intelligent information systems of the future. There are three basic approaches to integrating KBSs and databases. The first is to start with the KBS and incorporate data management functions. Alternatively, intelligence from the KBS can be incorporated into the database. Finally, the two systems can be allowed to co-exist as independent systems which can talk to each other by means of standard interfaces. There are many advantages to this last approach since it offers much more flexibility and extensibility and is consistent with the general trend in computing towards open systems.

Artificial Intelligence↗

PubMed1990

Relationship between carcinogenicity in rodents and the induction of sister chromatid exchanges and chromosomal aberrations in Chinese hamster ovary cells.

Two independent analyses were carried out to compare the induction of sister chromatid exchanges and of chromosomal aberrations as predictors of carcinogenicity. Using both a classical and a Bayesian approach, as well as by analysis of the structural fragments generated by CASE, an artificial intelligence system, it is included that individually neither of these tests is a satisfactory predictor of carcinogenicity. However, because the analysis revealed that each of the cytogenetic assays responds to a different set of structural features associated with carcinogenicity, it can be concluded that the assays can be included in a battery of tests to improve predictivity.

Animals↗

PubMed2003

Finding clusters of similar events within clinical incident reports: a novel methodology combining case based reasoning and information retrieval.

A novel methodological approach for identifying clusters of similar medical incidents by analyzing large databases of incident reports is described. The discovery of similar events allows the identification of patterns and trends, and makes possible the prediction of future events and the establishment of barriers and best practices. Two techniques from the fields of information science and artificial intelligence have been integrated--namely, case based reasoning and information retrieval--and very good clustering accuracies have been achieved on a test data set of incident reports from transfusion medicine. This work suggests that clustering should integrate the features of an incident captured in traditional form based records together with the detailed information found in the narrative included in event reports.

Artificial Intelligence↗

PubMed2001

Decision support system for medical triage.

The paper explains the application of an artificial intelligence tool for the purpose of medical decision-making. The first product of this application is a triage engine, available on the Internet, to help laypersons make a decision about the urgency of their situation by providing tailored and accurate information. The expansion of this tool can lead to diagnostic tools for professionals or for educational purposes.

Artificial Intelligence↗

PubMed1993

An aggregation of pro and con evidence for medical decision support systems.

One promising way to increase the classification accuracy of medical decision support systems is to implement heuristic combinations of pattern recognition and artificial intelligence tools. A parallel between "cognition" model and differential diagnostic task is sketched accentuating the aggregation of activating and restraining inputs and corresponding PRO and CON evidence in medicine. On the basis of this paradigm a trainable model of a fuzzy neuron is proposed which resembles some elements from the physician's decision process. An example from aviation medicine is presented which demonstrates the enhanced performance.

Aerospace Medicine↗

PubMed2006

A comprehensive overview of the applications of artificial life.

We review the applications of artificial life (ALife), the creation of synthetic life on computers to study, simulate, and understand living systems. The definition and features of ALife are shown by application studies. ALife application fields treated include robot control, robot manufacturing, practical robots, computer graphics, natural phenomenon modeling, entertainment, games, music, economics, Internet, information processing, industrial design, simulation software, electronics, security, data mining, and telecommunications. In order to show the status of ALife application research, this review primarily features a survey of about 180 ALife application articles rather than a selected representation of a few articles. Evolutionary computation is the most popular method for designing such applications, but recently swarm intelligence, artificial immune network, and agent-based modeling have also produced results. Applications were initially restricted to the robotics and computer graphics, but presently, many different applications in engineering areas are of interest.

Artificial Intelligence↗

PubMed2025

In vivo and in silico models of Drosophila for Parkinson's disease.

The fruit fly Drosophila melanogaster has emerged as an important model organism to shed light on neurodegeneration. Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder, the cause of which is still mostly unclear. The long-term use of available PD drugs may have major side effects, and they only target the symptoms without providing any effective cure for the disease. Therefore, in vivo and in silico approaches are extensively used to model PD-like phenotypes in Drosophila and investigate cellular alterations underlying PD pathogenesis. In vivo models are particularly crucial to provide insight into the PD-related molecular processes. It has been a preferred approach to investigate these models by collecting omics datasets, which can be further analysed using in silico modeling such as genome-scale metabolic models and artificial intelligence applications. This review aims to summarise in vivo and in silico modeling studies in the literature to illustrate the potential of the Drosophila in the characterisation of PD-related biological mechanisms towards providing early biomarkers and novel treatment options for PD.

Humans↗

PubMed1992

Implementing guidelines in ambulatory practice.

As we understand the process of ambulatory care better, the need to effectively implement standards of practice becomes more apparent. To facilitate successful use of practice guidelines, we have integrated an artificial intelligence system of Medical Logic Modules into our computerized medical record. A rule shell allows rapid development and prototyping of rules which can be practice reminders, information gathering utilities, or standing orders. A set of utilities allows non-programmer clinicians to develop and maintain the rule set. We will demonstrate these enhancements in the context of the comprehensive patient record.

Ambulatory Care↗

PubMed1994

Planning diagnostic imaging work-up strategies using case-based reasoning.

ISIS is a developmental decision support system that helps physicians select diagnostic imaging procedures. It uses case-based reasoning, an artificial-intelligence approach that emphasizes reasoning and planning from prior experience. The development, training, and evaluation of a prototype system were used to guide the development of ISIS. To realize a clinically useful system, particular emphasis has been placed on increasing the depth and breadth of case-based knowledge, enhancing the explanatory capabilities of the system, and refining the human-computer interfaces to include a critiquing approach.

Algorithms↗

PubMed1997

[A knowledge-based method for 3D segmentation and display of human brain].

From the view of the artificial intelligence, a new method of segmentation and display of human brain medical images is descrided. On the basis of the knowledge of brain anatomy and image processing, we have built the 3D knowledge mobel using frame as the main knowledge expression method. Under the guide of the knowledge, the main structures in the brain are segmented and displayed by means of the "inelligent ray-tracing".

Artificial Intelligence↗

PubMed1986

A new strategy for clinical decision making: censors and neuroendocrinological diseases.

A patient rarely has a single, isolated disease. The situation is usually much more complex since the different parts of the human organism and its metabolism interact with each other on multiple levels and follow several feedback patterns. These interactions and feedback patterns become even more complex when the effects of the external environment are considered. When several diseases are present, the first steps in medical diagnosis are to determine whether one of the diseases interacts with ("Censors") or changes the significant symptoms, signs, or results of the laboratory tests of the other diseases. We will try, within this paper, to go beyond the scope of the first generation of Artificial Intelligence systems in medicine to determine the effects of two diseases on each other. One important part of the effect of two diseases on each other is the effect of Censors. In addition, causal reasoning, reasoning by analogy, and learning from precedents are important and necessary for a human-like expert in medicine. Their application to thyroid diseases, with an implemented system, are considered in this paper.

Artificial Intelligence↗

PubMed1990

Neural networks in radiologic diagnosis. I. Introduction and illustration.

Artificial neural networks (NNs) process information in a manner similar to the way the human brain is thought to process information. Neural networks have potential application in radiology as an artificial intelligence technique that can provide computer-aided diagnostic assistance for the practicing radiologist. The basic characteristics of NNs and the manner in which information propagates through an NN are discussed in nontechnical language, to assist the diagnostic radiologist in understanding the basic principles of neurocomputing. Computer-aided diagnosis selection in pediatric chest radiography using NNs is discussed in a companion article.

Artificial Intelligence↗

PubMed2005

Evolutionary combinatorial chemistry, a novel tool for SAR studies on peptide transport across the blood-brain barrier. Part 2. Design, synthesis and evaluation of a first generation of peptides.

The use of high-throughput methods in drug discovery allows the generation and testing of a large number of compounds, but at the price of providing redundant information. Evolutionary combinatorial chemistry combines the selection and synthesis of biologically active compounds with artificial intelligence optimization methods, such as genetic algorithms (GA). Drug candidates for the treatment of central nervous system (CNS) disorders must overcome the blood-brain barrier (BBB). This paper reports a new genetic algorithm that searches for the optimal physicochemical properties for peptide transport across the blood-brain barrier. A first generation of peptides has been generated and synthesized. Due to the high content of N-methyl amino acids present in most of these peptides, their syntheses were especially challenging due to over-incorporations, deletions and DKP formations. Distinct fragmentation patterns during peptide cleavage have been identified. The first generation of peptides has been studied by evaluation techniques such as immobilized artificial membrane chromatography (IAMC), a cell-based assay, log Poctanol/water calculations, etc. Finally, a second generation has been proposed.

Algorithms↗

PubMed2026

Zebrafish as a versatile model in biomedical research, from disease modeling to regenerative medicine: a review.

Zebrafish are an effective animal model widely utilized in biomedical research. They are known for their rapid reproduction and substantial genetic similarity to humans. Their transparent embryos directly enable the visualization of developmental processes and disease progression. This makes zebrafish invaluable for studying a broad range of human diseases, including cancer, cardiovascular disorders, and neurodegenerative conditions. Compared with other vertebrate models, zebrafish offer several advantages, including ease of genome editing, cost-effective maintenance, and suitability for high-throughput drug screening. Recent advancements have expanded the use of zebrafish in disease modeling and regenerative medicine, providing deeper insights into the genetic and cellular mechanisms underlying human pathologies. Zebrafish provide a robust platform for evaluating the safety, efficacy, and regenerative potential of both natural and synthetic biomaterials, including hydroxyapatite, bioactive glass nanoparticles, and bioceramics. This capability facilitates the creation of artificial tissues that closely resemble native structures. Additionally, integrating artificial intelligence technologies has improved automated data analysis and phenotyping in zebrafish studies, enhancing both accuracy and throughput. This review highlights current applications of zebrafish in disease modeling, drug discovery, regenerative medicine, and biomaterial assessment, emphasizing their evolving role as a versatile preclinical platform supported by advanced genetic and computational tools.

Animals↗