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Results for “artificial intelligence”

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At least 1,351 records · Page 75Linked to original sources
PubMed1998

Improving morphology-based malignancy grading schemes in astrocytic tumors by means of computer-assisted techniques.

We propose an original methodology which improves the accuracy of the prognostic values associated with conventional morphologically-based classifications in supratentorial astrocytic tumors in the adult. This methodology may well help neuropathologists, who must determine the aggressiveness of astrocytic tumors on the basis of morphological criteria. The proposed methodology comprises two distinct steps, i.e. i) the production of descriptive quantitative variables (related to DNA ploidy level and morphonuclear aspects) by means of computer-assisted microscopy and ii) data analysis based on an artificial intelligence-related method, i.e. the decision tree approach. Three prognostic problems were considered on a series of 250 astrocytic tumors including 39 astrocytomas (AST), 47 anaplastic astrocytomas (ANA) and 164 glioblastomas (GBM) identified in accordance with the WHO classification. These three problems concern i) variations in the aggressiveness level of the high-grade tumors (ANA and GBM), ii) the detection of the aggressive as opposed to the less aggressive low-grade astrocytomas (AST), and iii) the detection of the aggressive as opposed to the less aggressive anaplastic astrocytomas (ANA). Our results show that the proposed computer-aided methodology improves conventional prognosis based on conventional morphologically-based classifications. In particular, this methodology enables some reference points to be established on the biological continuum according to the sequence AST-->ANA-->GBM.

Adult↗
PubMed1997

Hidden Markov model analysis of motifs in steroid dehydrogenases and their homologs.

The increasing size of protein sequence databases is straining methods of sequence analysis, even as the increased information offers opportunities for sophisticated analyses of protein structure, function, and evolution. Here we describe a method that uses artificial intelligence-based algorithms to build models of families of protein sequences. These models can be used to search protein sequence databases for remote homologs. The MEME (Multiple Expectation-maximization for Motif Elicitation) software package identifies motif patterns in a protein family, and these motifs are combined into a hidden Markvov model (HMM) for use as a database searching tool. Meta-MEME is sensitive and accurate, as well as automated and unbiased, making it suitable for the analysis of large datasets. We demonstrate Meta-MEME on a family of dehydrogenases that includes mammalian 11 beta-hydroxysteroid and 17 beta-hydroxysteroid dehydrogenase and their homologs in the short chain alcohol dehydrogenase family. We chose this dataset because it is large and phylogenetically diverse, providing a good test of the sensitivity and selectivity of Meta-MEME on a protein family of biological interest. Indeed, Meta-MEME identifies at least 350 members of this family in Genpept96 and clearly separates these sequences from non-homologous proteins. We also show how the MEME motif output can be used for phylogenetic analysis.

Algorithms↗
PubMed2026

Digital and computational morphology in hematology: current platforms, clinical evidence, and future requirements.

INTRODUCTION: Morphologic examination of peripheral blood and bone marrow remains central to the diagnosis and classification of hematologic disorders. Conventional optical microscopy, however, is labor-intensive, dependent on operator expertise, and affected by interobserver variability. Digital morphology has developed from automated image acquisition and cell pre-classification into a broader field that includes whole-slide imaging, remote review, quantitative morphometry, and artificial intelligence-based analysis. CONTENT: This review examines current applications of digital morphology in peripheral blood, bone marrow aspirates, malaria detection, and body-fluid analysis. Commercial platforms are evaluated with particular attention to the distinction between raw automated pre-classification, expert digital post-classification, and comparison with independent optical microscopy. Digital systems generally perform well for common mature leukocyte populations but remain less reliable for rare or diagnostically critical cells, including blasts, abnormal lymphoid cells, plasma cells, and intermediate maturation stages. Research systems increasingly extend analysis from individual-cell classification to whole-slide, specimen-level, and patient-level assessment. SUMMARY: Digital morphology can improve standardization, image traceability, remote consultation, education, proficiency testing, quality assurance, and selected aspects of laboratory workflow. Its clinical value depends on appropriate validation, transparent reporting of reference methods, recognition of algorithm-specific failure modes, and clearly defined criteria for expert review and conventional microscopy. Human expertise remains essential not only for validating results but also for adapting cell taxonomies and interpretive rules to evolving classifications of hematologic diseases. OUTLOOK: Future progress will require representative multicenter datasets, harmonized morphologic terminology, external validation, interoperability with laboratory information systems, and continuous monitoring after software or hardware updates. Integration of morphology with quantitative hematology, flow cytometry, cytogenetics, genomics, and clinical data may support more comprehensive computational diagnosis. Digital platforms may also broaden access to specialist expertise, training, and quality programs in resource-limited institutions and regions, provided that infrastructure, governance, and professional competency are adequately supported.

artificial intelligence↗
PubMed2005

New type of dry substances content meter using microwaves for application in biogas plants.

Dry substances (DS) are an important index for monitoring and controlling anaerobic co-digestion in biogas plants. We have developed and tested an online meter that measures suspended solids by means of the reflection coefficient of an exiting microwave signal, which is dependent on the dielectric properties of the suspensions. Intelligent models based on partial least squares regression (PLSR) and artificial neural network (ANN) for calibration allow exact and reproducible measurements under different circumstances. This measuring method is appropriate for contactless and online measurements of dry substance contents in biogas plants in a large range from 2-14%.

Agriculture↗
PubMed1995

[Taste sensor].

Explore the source record for details and available documents.

Artificial Intelligence↗
PubMed2001

Neural networks for improved target differentiation and localization with sonar.

This study investigates the processing of sonar signals using neural networks for robust differentiation of commonly encountered features in indoor robot environments. Differentiation of such features is of interest for intelligent systems in a variety of applications. Different representations of amplitude and time-of-flight measurement patterns acquired from a real sonar system are processed. In most cases, best results are obtained with the low-frequency component of the discrete wavelet transform of these patterns. Modular and non-modular neural network structures trained with the back-propagation and generating-shrinking algorithms are used to incorporate learning in the identification of parameter relations for target primitives. Networks trained with the generating-shrinking algorithm demonstrate better generalization and interpolation capability and faster convergence rate. Neural networks can differentiate more targets employing only a single sensor node, with a higher correct differentiation percentage (99%) than achieved with previously reported methods (61-90%) employing multiple sensor nodes. A sensor node is a pair of transducers with fixed separation, that can rotate and scan the target to collect data. Had the number of sensing nodes been reduced in the other methods, their performance would have been even worse. The success of the neural network approach shows that the sonar signals do contain sufficient information to differentiate all target types, but the previously reported methods are unable to resolve this identifying information. This work can find application in areas where recognition of patterns hidden in sonar signals is required. Some examples are system control based on acoustic signal detection and identification, map building, navigation, obstacle avoidance, and target-tracking applications for mobile robots and other intelligent systems.

Algorithms↗
PubMed2006

Dynamics of sequential decision making.

We suggest a new paradigm for intelligent decision-making suitable for dynamical sequential activity of animals or artificial autonomous devices that depends on the characteristics of the internal and external world. To do it we introduce a new class of dynamical models that are described by ordinary differential equations with a finite number of possibilities at the decision points, and also include rules solving this uncertainty. Our approach is based on the competition between possible cognitive states using their stable transient dynamics. The model controls the order of choosing successive steps of a sequential activity according to the environment and decision-making criteria. Two strategies (high-risk and risk-aversion conditions) that move the system out of an erratic environment are analyzed.

Animals↗
PubMedDate not supplied

The development of embodied cognition: six lessons from babies.

The embodiment hypothesis is the idea that intelligence emerges in the interaction of an agent with an environment and as a result of sensorimotor activity. We offer six lessons for developing embodied intelligent agents suggested by research in developmental psychology. We argue that starting as a baby grounded in a physical, social, and linguistic world is crucial to the development of the flexible and inventive intelligence that characterizes humankind.

Artificial Intelligence↗