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PubMed2006

Seamless image stitching by minimizing false edges.

Various applications such as mosaicing and object insertion require stitching of image parts. The stitching quality is measured visually by the similarity of the stitched image to each of the input images, and by the visibility of the seam between the stitched images. In order to define and get the best possible stitching, we introduce several formal cost functions for the evaluation of the stitching quality. In these cost functions the similarity to the input images and the visibility of the seam are defined in the gradient domain, minimizing the disturbing edges along the seam. A good image stitching will optimize these cost functions, overcoming both photometric inconsistencies and geometric misalignments between the stitched images. We study the cost functions and compare their performance for different scenarios both theoretically and practically. Our approach is demonstrated in various applications including generation of panoramic images, object blending and removal of compression artifacts. Comparisons with existing methods show the benefits of optimizing the measures in the gradient domain.

Algorithms↗

PubMed1998

Self-adaptation in evolving systems.

A theoretical and experimental analysis is made of the effects of self-adaptation in a simple evolving system. Specifically, we consider the effects of coding the mutation and crossover probabilities of a genetic algorithm evolving in certain model fitness landscapes. The resultant genotype-phenotype mapping is degenerate in fitness space, there being no direct selective advantage for one probability versus another. Thus there is a "symmetry" between various genotypes that all correspond to the same phenotype. We show that the action of mutation and crossover lifts this degeneracy, that is, the genetic operators induce a breaking of the genotype-phenotype symmetry, thus leading to a preference for those genotypes that propagate most successfully into future generations. We demonstrate that this induced symmetry breaking allows the system to self-adapt in a time-dependent environment.

Adaptation, Physiological↗

PubMed1999

Development of a reminder system for general practitioners.

In this contribution we describe a prototype reminder system that has been developed to influence General Practitioners' (GP) test request behaviour. It is intended to substitute conventional written feedback by human experts. The system generates critical comments about the rationality of the test request at the moment the GP requests a test that is not in line with national or regional guidelines.

Artificial Intelligence↗

PubMed1993

Structured data collection and knowledge-based user guidance for abdominal ultrasound reporting.

This paper describes a system for structured data collection and report generation in abdominal ultrasonography. The system is based on a controlled vocabulary and hierarchies of concepts; it uses a graphical user interface. More than 17,000 reports have been generated by 43 physicians using this system, which is integrated into a departmental information system. Evaluations have shown that it is a well accepted tool for the fast generation of reports of comparatively high quality. The functionality is enhanced by two additional components: a hybrid knowledge-based module for "intelligent" user guidance and an interactive tutoring system to illustrate the terminology.

Abdomen↗

PubMed1989

Knowledge engineering for clinical consultation programs: modeling the application area.

Developers of computer-based decision-support tools frequently adopt either pattern recognition or artificial intelligence techniques as the basis for their programs. Because these developers often choose to accentuate the differences between these alternative approaches, the more fundamental similarities are frequently overlooked. The principal challenge in the creation of any clinical consultation program - regardless of the methodology that is used - lies in creating a computational model of the application domain. The difficulty in generating such a model manifests itself in symptoms that workers in the expert systems community have labeled "the knowledge-acquisition bottleneck" and "the problem of brittleness". This paper explores these two symptoms and shows how the development of consultation programs based on pattern-recognition techniques is subject to analogous difficulties. The expert systems and pattern recognition communities must recognize that they face similar challenges, and must unite to develop methods that assist with the process of building of models of complex application tasks.

Decision Support Techniques↗

PubMedDate not supplied

Case-based tutoring from a medical knowledge base.

The past decade has seen the emergence of programs that make use of large knowledge bases to assist physicians in diagnosis within the general field of internal medicine. One such program, Internist-I, contains knowledge about over 600 diseases, covering a significant proportion of internal medicine. This paper describes the process of converting a subset of this knowledge base--in the area of cardiovascular diseases--into a probabilistic format, and the use of this resulting knowledge base to teach medical diagnostic knowledge. The system (called KBSimulator--for Knowledge-Based patient Simulator) generates simulated patient cases and uses these cases as a focal point from which to teach medical knowledge. This project demonstrates the feasibility of building an intelligent, flexible instructional system that uses a knowledge base constructed primarily for medical diagnosis.

Artificial Intelligence↗

PubMed1999

Refining instructional text generation after evaluation.

In this paper, we describe how user-adapted explanations about drug prescriptions can be generated from already existing data sources. We start by illustrating the two-step approach employed in the first version of the natural language generator and the limitations of generated texts, that we discovered through analytical and empirical evaluations. We claim that, although style refinement would be needed in these texts, particular care should be devoted to implementing some of the persuasion techniques that doctors employ in their explanations. This would require either thoroughly revising the text planning techniques employed or converting to a multistep generation architecture. We justify why we selected this second alternative and propose some heuristics to repair problems found in the first version of the generator. Some final considerations about the advantages of this approach and the possibility of generalizing it to other domains conclude the paper.

Adrenergic beta-Antagonists↗

PubMedDate not supplied

Predicting protein-ligand binding affinities using novel geometrical descriptors and machine-learning methods.

Inspired by the concept of knowledge-based scoring functions, a new quantitative structure-activity relationship (QSAR) approach is introduced for scoring protein-ligand interactions. This approach considers that the strength of ligand binding is correlated with the nature of specific ligand/binding site atom pairs in a distance-dependent manner. In this technique, atom pair occurrence and distance-dependent atom pair features are used to generate an interaction score. Scoring and pattern recognition results obtained using Kernel PLS (partial least squares) modeling and a genetic algorithm-based feature selection method are discussed.

Algorithms↗

PubMed2002

Architecture of a mediator for a bioinformatics database federation.

Developments in our ability to integrate and analyze data held in existing heterogeneous data resources can lead to an increase in our understanding of biological function at all levels. However, supporting ad hoc queries across multiple data resources and correlating data retrieved from these is still difficult. To address this, we are building a mediator based on the functional data model database, P/FDM, which integrates access to heterogeneous distributed biological databases. Our architecture makes use of the existing search capabilities and indexes of the underlying databases, without infringing on their autonomy. Central to our design philosophy is the use of schemas. We have adopted a federated architecture with a five-level schema, arising from the use of the ANSI-SPARC three-level schema to describe both the existing autonomous data resources and the mediator itself. We describe the use of mapping functions and list comprehensions in query splitting, producing execution plans, code generation, and result fusion. We give an example of cross-database querying involving data held locally in P/FDM systems and external data in SRS.

Algorithms↗

PubMed1998

Clinical data entry.

Routine capture of patient data for a computer-based patient record system remains a subject of study. Time constraints that require fast data entry and maximal expression power are in favor of free text data entry. However, using patient data directly for decision support systems, for quality assessment, etc. requires structured data entry, which appears to be more tedious and time consuming. In this paper, a prototype clinical data entry application is described that combines free text and structured data entry in one single application and allows clinicians to smoothly switch between these two different input styles. A knowledge base involving a semantic network of clinical data entry terms and their properties and relationships is used by this application to support structured data entry. From structured data, sentences are generated and shown in a text processor together with the free text. This presentation metaphor allows for easy integrated presentation of structured data and free text.

Artificial Intelligence↗

PubMed2026

Integrating Radiogenomics and CSF-Based Liquid Biopsy Sequencing for Precision Neuro-Oncology.

Glioblastoma and diffuse gliomas pose major therapeutic challenges due to marked intratumoral heterogeneity, limited tissue accessibility, and the blood-brain barrier. Tissue-based next-generation sequencing (NGS) remains essential for WHO CNS5 molecular classification, yet it is invasive and poorly suited to serial monitoring. Two complementary non- or minimally invasive approaches have advanced rapidly: radiogenomics, which correlates multiparametric MRI features with genomic alterations, and cerebrospinal fluid (CSF) liquid biopsy sequencing, which detects circulating tumor DNA with high tissue concordance. This review examines the independent progress and synergistic integration of radiogenomics and CSF-NGS. Imaging signatures can non-invasively predict key drivers (IDH1/2, EGFR, TERT, PTEN, TP53) and molecular subtypes, while CSF-ctDNA sequencing enables real-time assessment of clonal evolution, therapy resistance (including post-temozolomide hypermutation), and residual disease. We discuss technical considerations, performance metrics, multimodal artificial-intelligence fusion, and emerging clinical applications for diagnosis, prognosis, treatment selection, and longitudinal surveillance. Critical challenges, standardization, prospective validation, and workflow integration are highlighted. By combining the spatial phenotypic information of radiogenomics with the temporal genomic resolution of CSF sequencing, this multimodal strategy offers a promising path toward precision neuro-oncology and reduced reliance on repeated invasive sampling.

Humans↗

PubMed2005

Non-Gaussian smoothing of low-count transmission scans for PET whole-body studies.

A non-Gaussian smoothing (NGS) technique is developed for filtering low count transmission (TR) data to be used for attenuation correction (AC) of positron emission tomography (PET) studies. The method is based on a statistical technique known as the generalized linear mixed model that allows an inverse link function that avoids the inversion of the observed transmission data. The NGS technique has been implemented in the sinogram domain in one-dimensional mode as angle-by-angle computation. To make it adaptive as a function of the TR count statistics we also develop and validate an objective procedure to choose an optimal smoothing parameter. The technique is assessed using experimental phantoms, simulating PET whole-body studies, and applied to real patient data. Different experimental conditions, in terms of TR scan time (from 1 h to 1 min), covering a wide range of TR counting statistic are considered. The method is evaluated, in terms of mean squared error (MSE), by comparing pixel by pixel the distribution for high counts statistics TR scan (1 h) with the corresponding counts distribution for low count statistics TR scans (e.g., 1 min). The smoothing parameter selection is shown to have high efficiency, meaning that it tends to choose values close to the unknown best value. Furthermore, the counts distribution of emission (EM) images, reconstructed with AC generated using low count TR data (1 min), are within 5% of the corresponding EM images reconstructed with AC generated using the high count statistics TR data (1 h). An application to a real patient whole-body PET study shows the promise of the technique for routine use.

Algorithms↗

PubMedDate not supplied

A pipeline for computer aided polyp detection.

We present a novel pipeline for computer-aided detection (CAD) of colonic polyps by integrating texture and shape analysis with volume rendering and conformal colon flattening. Using our automatic method, the 3D polyp detection problem is converted into a 2D pattern recognition problem. The colon surface is first segmented and extracted from the CT data set of the patient's abdomen, which is then mapped to a 2D rectangle using conformal mapping. This flattened image is rendered using a direct volume rendering technique with a translucent electronic biopsy transfer function. The polyps are detected by a 2D clustering method on the flattened image. The false positives are further reduced by analyzing the volumetric shape and texture features. Compared with shape based methods, our method is much more efficient without the need of computing curvature and other shape parameters for the whole colon surface. The final detection results are stored in the 2D image, which can be easily incorporated into a virtual colonoscopy (VC) system to highlight the polyp locations. The extracted colon surface mesh can be used to accelerate the volumetric ray casting algorithm used to generate the VC endoscopic view. The proposed automatic CAD pipeline is incorporated into an interactive VC system, with a goal of helping radiologists detect polyps faster and with higher accuracy.

Algorithms↗

PubMed2006

SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms.

BACKGROUND: The development of algorithms to infer the structure of gene regulatory networks based on expression data is an important subject in bioinformatics research. Validation of these algorithms requires benchmark data sets for which the underlying network is known. Since experimental data sets of the appropriate size and design are usually not available, there is a clear need to generate well-characterized synthetic data sets that allow thorough testing of learning algorithms in a fast and reproducible manner. RESULTS: In this paper we describe a network generator that creates synthetic transcriptional regulatory networks and produces simulated gene expression data that approximates experimental data. Network topologies are generated by selecting subnetworks from previously described regulatory networks. Interaction kinetics are modeled by equations based on Michaelis-Menten and Hill kinetics. Our results show that the statistical properties of these topologies more closely approximate those of genuine biological networks than do those of different types of random graph models. Several user-definable parameters adjust the complexity of the resulting data set with respect to the structure learning algorithms. CONCLUSION: This network generation technique offers a valid alternative to existing methods. The topological characteristics of the generated networks more closely resemble the characteristics of real transcriptional networks. Simulation of the network scales well to large networks. The generator models different types of biological interactions and produces biologically plausible synthetic gene expression data.

Algorithms↗

PubMed2026

Insecticidal peptides as sustainable tools for future agriculture.

The increasing global human population and the intensification of agriculture present unprecedented challenges for pest control. The escalating resistance of pests to conventional synthetic insecticides, coupled with ecological and health concerns, underscores the urgent need for innovative and sustainable management approaches. Insecticidal peptides, due to their structural diversity, molecular specificity, and biodegradability, are emerging as promising candidates for the development of next-generation bioinsecticides. This strategic roadmap synthesizes recent advances in peptide architectures, ranging from pore-forming scaffolds to designs targeting enzyme inhibition and mimicking neuroendocrine actions, with a focus on the molecular mechanisms underpinning their selectivity and efficacy. By integrating structure-function insights with translational frameworks, we identify critical knowledge gaps and propose a pathway toward biotechnological tools, including bioinspired synthesis, artificial intelligence (AI)-guided peptide engineering, and nanodelivery systems for controlled release. Our analysis positions peptide-based insecticides at the forefront of sustainable agriculture, with the potential to minimize off-target effects, reduce environmental impact, and enhance crop resilience in the face of global change.

Agricultural biotechnology↗

PubMedDate not supplied

Neural networks in radiology: an introduction and evaluation in a signal detection task.

Neural networks are a computer architecture, implementable in software or hardware, that allow an entirely new approach to the computerized perception of data. These so-called connectionist models are inspired by what is known about the architecture of biological neurons, in which the "intelligence" or processing capability of the network is a result of the interconnection strengths between large arrays of nonlinear processing nodes. Neural networks are described and then are used to analyze the common radiological problem of pattern recognition on a noisy background. Classical signal detection theory is used to compare network performance against that of human observers, using computer-generated sets of very simple "nodules." The neural network performed with better accuracy, relative to human observer performance, in the detection of this elementary test object. Although these results may not scale up with more complex images, the favorable performance of neural networks at this level suggests that further investigation is warranted.

Artificial Intelligence↗

PubMed2006

Automatic generation of spoken dialogue from medical plans and ontologies.

This paper presents some research undertaken as part of the EU-funded HOMEY project, into the application of intelligent dialogue systems to healthcare systems. The work presented here concentrates on the ways in which knowledge of underlying task structure (e.g., a medical guideline) can be combined with ontological knowledge (e.g., medical semantic dictionaries) to provide a basis for the automatic generation of flexible and re-configurable dialogue. This approach is next evaluated via a specific application that provides decision support to general practitioners to help determine whether or not a patient should be referred to a cancer specialist. The competence of the resulting dialogue application, its speech recognition performance, and dialogue performance are all evaluated to determine the applicability of this approach.

Artificial Intelligence↗

PubMed2025

AI-HOPE: an AI-driven conversational agent for enhanced clinical and genomic data integration in precision medicine research.

MOTIVATION: The growing complexity of clinical cancer research has fueled a surge in demand for automated bioinformatics tools capable of integrating clinical and genomic data to accelerate discovery efforts. RESULTS: We present the Artificial Intelligence Agent for High-Optimization and Precision Medicine (AI-HOPE), an AI-driven system that enables domain experts to conduct integrative data analyses through natural language interactions. Powered by Large Language Models, AI-HOPE interprets user instructions, converts them into executable code, and autonomously analyzes locally stored data. It supports flexible association studies, subset comparisons, clinical prevalence assessments and survival analyses. In addition, AI-HOPE enables global variable scans to identify features significantly associated with a user-defined outcome, making a powerful and intuitive tool for advancing precision medicine research. Importantly, its closed-system design prevents clinical data leakage. To demonstrate its utility, AI-HOPE was applied to The Cancer Genome Atlas data to address two clinical questions. First, it identified significant enrichment of TP53 mutations in late-stage colorectal cancer compared to early-stage cases. Second, it uncovered a strong association between KRAS mutations and poorer progression-free survival in FOLFOX-treated patients. These findings align with established literature and demonstrate AI-HOPE's ability to generate meaningful insights independently, without prior assumptions. By removing programming barriers and simplifying complex analyses, AI-HOPE bridges the gap between data complexity and research needs. With its scalable and adaptable framework, AI-HOPE has the potential to support diverse biomedical research fields, driving innovation and efficiency in translational studies. AVAILABILITY AND IMPLEMENTATION: The AI-HOPE software and demonstration data is available at https://github.com/Velazquez-Villarreal-Lab/AI-HOPE.

Precision Medicine↗