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PubMed1994

Schizophrenic deficits: neuroleptics and the prefrontal cortex.

Modern techniques have been applied to brain modeling, based on recent approaches in the artificial intelligence field that use brain-like "connectionistic" computational architectures. The model proposed by Cohen and Servan-Schreiber uses a gain parameter which they identify with dopamine function. They apply their model to neuroleptically treated schizophrenia patients who show improved task performance which they link to increased dopamine function and increased gain in the prefrontal cortex. However, evidence indicates that antipsychotic medications block dopamine (especially D2) receptors, decreasing mesolimbic and mesocortical dopamine function. If therapeutic dosages of neuroleptics diminish dopamine function, this would decrease gain in context modules needed for adequate task performance. Schizophrenia patients would perform more poorly by further reducing gain in their already compromised context modules. The current investigators suggest three possible ways to resolve this difficulty, to explain why normals perform more poorly when taking neuroleptics, although acute schizophrenia patients' performance may be enhanced in several areas. Evidence would suggest that multiple processes occur simultaneously in neuroleptically treated patients with some processes counterbalancing others.

Antipsychotic Agents↗

PubMed1983

Critiquing anesthetic management: the "ATTENDING" computer system.

ATTENDING, a computer system under development using Artificial Intelligence techniques, is designed to critique an anesthetist's preoperative plan for anesthetic management. The system receives as input: 1) a list of a patient's medical problems; 2) a planned surgical procedure; and 3) an anesthetic plan outlining the agents and techniques to be used for premedication, induction, intubation, and maintenance of general or regional anesthesia. ATTENDING critiques this plan, discussing the risks and benefits of the proposed approach and of other reasonable approaches. To critique a physician's plan, the ATTENDING system must be able to 1) explore alternative approaches to a patient's management in a flexible fashion; 2) assess the relative risks and benefits of these alternatives; and 3) produce a readable, easily understood analysis written in English prose. This article describes how these design issues are addressed in ATTENDING's current implementation. The ATTENDING system is presently being used in a tutorial mode which allows self-evaluation by an anesthetist.

Anesthesia↗

PubMedDate not supplied

Uncertainty based approach for symbolic classification of numeric variables in intensive care units.

In intensive care units (ICUs), certain parameters must be interpreted taking into account the intrinsic characteristics of each patient or the peculiarities of the clinical case under consideration. Similar temporal evolutions of some parameters in different patients could have different interpretations. Artificial intelligence techniques can aid in resolving this problem through the construction of expert systems (ES). These systems are capable of performing contextual evaluations of the parameters typically monitored in ICUs. This contextual evaluation is usually carried out using symbolic elements. Thus, the symbolic processing of numeric data is an important task to perform. In any event, the assignment of semantic labels to numeric values is always an uncertain and arbitrary process. This suggests the convenience of defining and implementing representation schemes capable of dealing with uncertain knowledge. This paper presents a model for the symbolic processing of numeric variables in which the uncertainty associated with the assignment of literals appears spontaneously. The categorical approach for the symbolic classification of numeric values is a particular case of the proposed model.

Data Interpretation, Statistical↗

PubMed1990

Computer-aided diagnosis in chest radiology.

Digital radiography offers several important advantages over conventional systems, including abilities for image manipulation, transmission, and storage. In the long term, however, the unique ability to apply artificial intelligence techniques for automated detection and quantitation of disease may have an even greater impact on radiologic practice. Although CAD is still in its infancy, the results of several recent studies clearly indicate a major potential for the future. The concept of using computers to analyze medical images is not new, but recent advances in computer technology together with progress in implementing practical digital radiography systems have stimulated research efforts in this exciting field. Several facets of CAD are presently being developed at the University of Chicago and elsewhere for application in chest radiology as well as in mammography and vascular imaging. To date, investigators have focused on a limited number of subjects that have been, by their nature, particularly suitable for computer analysis. There is no aspect of radiologic diagnosis that could not potentially benefit from this approach, however. The ultimate goal of these endeavors is to provide a system for comprehensive automated image analysis, the results of which could be accepted or modified at the discretion of the radiologist.

Breast Diseases↗

PubMed1998

Back propagation neural network in the discrimination of benign from malignant lower urinary tract lesions.

PURPOSE: We investigated the potential value of morphometry and artificial intelligence tools to discriminate between benign and malignant lower urinary tract lesions. MATERIALS AND METHODS: The lesions included lithiasis in 50 cases, inflammation in 61, benign prostatic hyperplasia in 99, carcinoma in situ in 5, and grade I and grades II and III transitional cell carcinoma of the bladder in 71 and 184, respectively. Images of routine processed voided urine smears stained by the Giemsa technique were analyzed using a custom image analysis system, providing a data set of 45,452 cells. A neural net model of the back propagation type was used to discriminate benign from malignant cells based on the extracted morphometric and textural features. Data from 13,636 randomly selected cells (30% of the total data) were used as a training set and the data from the remaining 31,816 cells comprised the test set. In a similar attempt to discriminate at the patient level data on 30% of those randomly selected were used to train a back propagation neural net and data on the remaining 329 were used for testing. RESULTS: Application of the back propagation neural net enabled the correct classification of 95.34% of benign and 86.71% of malignant cells with overall 90.57% accuracy. At the patient level the back propagation neural net enabled the correct classification of 100% of those with benign and 94.51% of those with malignant disease with overall 96.96% accuracy. CONCLUSIONS: The use of neural nets and image morphometry may increase the speed of cytological diagnosis and the diagnostic accuracy of voided urine cytology.

Carcinoma in Situ↗

PubMed1999

Prediction of tacrolimus blood levels by using the neural network with genetic algorithm in liver transplantation patients.

The neural network (NN) is a technique using an artificial intelligent concept in predicting outcomes by using various input variables. Tacrolimus pharmacokinetics has wide inter- and intra-subject variability and it is often difficult to predict its blood concentrations by dose alone. The objectives of this study are to select the clinically significant variables and to predict the blood concentration of tacrolimus in liver transplant patients by NN combined with genetic algorithm (GA). A total of thirty-two adult liver transplant patients from the University of Iowa Hospitals and Clinics were selected and the patients' data were retrospectively collected. These patient were randomly assigned into two groups: either the training group (n = 10), or testing group (n = 22). A back propagation (BP) NN was developed which contained two hidden layers. A dynamic BP NN based on the time series concept was trained by using the current and previous data sets to predict the trough levels of tacrolimus. The mean of the NN prediction for tacrolimus blood levels was not significantly different from the observed value by a paired t-test comparison (12.05+/-2.67 ng/ml vs. 12.14+/-2.64 ng/ml, p = 0.80). The average difference of the testing sets between the observed and predicted levels was 1.74 ng/ml with a range from 0.08 to 5.26 ng/ml which is clinically acceptable range. Thirty-seven out of 44 data sets (84%) in the testing group were within 3.0 ng/ml of the observed values. This study demonstrated that tacrolimus blood concentrations are precisely predictable in liver transplant patients using patients variables by NN.

Adult↗

PubMed1995

Symbiosis of nurse and machine through fuzzy logic: improved specificity of a neonatal pulse oximeter alarm.

This article describes the theoretical underpinnings that preceded the design of a new neonatal pulse oximeter alarm that differentiates true from false alarms based on an artificial intelligence theory called "fuzzy logic." The connection between the intuition and sense of advocacy gained through neonatal primary nursing and the application of fuzzy logic to solve the problem of false alarms--that is, the symbiosis between nurse and machine--are explained. Emphasis is placed throughout on the importance of involving nurses in the development of the technology for which they are responsible.

Decision Making↗

PubMed2026

SurvGRN: a multi-feature fusion framework for bladder cancer survival prediction.

Bladder cancer survival outcomes exhibit significant heterogeneity, influenced by multifaceted factors. While digital pathology-based survival models leveraging artificial intelligence show promise, they often overlook complementary data sources. Conversely, imaging lacks cellular detail, and genomics/proteomics entail complexity and cost. To integrate multidimensional data for enhanced survival prediction, we propose SurvGRN, a multi-feature fusion framework. SurvGRN synergistically combines clinical variables, transcriptomics, and digital pathology slides using a gated residual network architecture. Pathological features are extracted via multiple instance learning, while clinical and transcriptomic data are processed as static inputs. These features are dynamically fused using a long short-term memory (LSTM) network for comprehensive survival risk assessment. Evaluated on 400 bladder cancer patients, SurvGRN significantly outperformed existing methods: improving the C-index by 12.6% over DeepMISL; 20.6% and 7.1% over graph-based models (DeepGraphConv and Patch-GCN); and 5.4% and 4.0% over attention-based approaches (Surformer and HVTSurv). Ablation studies confirmed the contributions of pathology features (extracted via ResNet-50 pre-trained on bladder tissue), clinical/transcriptomic data, and the LSTM fusion. SurvGRN also enabled significant stratification of patients into distinct risk cohorts. This work demonstrates that holistic integration of multi-source data through tailored fusion architectures substantially improves bladder cancer survival prediction.

bladder cancer↗

PubMed2026

Immune dysregulatory disorders: perspective from solving a diagnostic odyssey.

PURPOSE OF REVIEW: Inborn errors of immunity (IEIs), once considered rare disorders characterized primarily by recurrent infections, are now recognized as a rapidly expanding group of diseases encompassing autoimmunity, autoinflammation, allergy, malignancy, and immune dysregulation. Advances in next-generation sequencing, functional immunology, and systems biology have revealed overlap between traditionally distinct disease categories and highlighted the complexity of genotype-phenotype relationships. RECENT FINDINGS: While this evolution has led to the discovery of hundreds of previously unrecognized disorders, it has also challenged conventional diagnostic paradigms and demonstrated how patients may have care spread across multiple specialties, without a clear medical home. These discoveries have also highlighted ongoing challenges translating scientific findings to the clinic including difficulties in accessing genomic testing, interpretation of variants of uncertain significance, impacts of incomplete penetrance and somatic mosaicism, and limited availability of specialized functional assays. Emerging computational approaches, including artificial intelligence, offer opportunities to accelerate diagnosis but cannot replace comprehensive clinical evaluation or longitudinal physician-patient relationships. SUMMARY: This perspective examines how the diagnostic odyssey for immune dysregulatory disorders has evolved, side-by-side with the changing framework for diagnosing rare immune diseases. We propose an integrated approach combining clinical phenotyping, genomics, functional validation, and multidisciplinary expertise to unite ongoing discovery between clinicians and scientists, diagnostics, and patient outcomes.

diagnostic odyssey↗

PubMed2025

Innovative strategies for mitochondrial dysfunction in myeloproliferative neoplasms a step toward precision medicine.

Myeloproliferative neoplasms (MPNs) are clonal disorders of hematopoietic stem cells characterized by aberrant proliferation of myeloid lineages, driven primarily by mutations in JAK2, CALR, and myeloproliferative leukemia, leading to constitutive activation of the JAK-STAT pathway. Emerging evidence highlights mitochondrial dysfunction as a key factor in MPN pathogenesis, contributing to increased reactive oxygen species production, mitochondrial DNA mutations, and dysregulated mitochondrial dynamics, which collectively promote clonal expansion and apoptosis resistance. Targeting mitochondrial pathways has gained attention as a therapeutic strategy, with approaches including mitochondria-targeted antioxidants, metabolic inhibitors, and modulation of mitophagy and mitochondrial fission/fusion dynamics. However, challenges such as drug delivery specificity, therapeutic resistance, and off-target effects remain significant. Recent advances in precision medicine, incorporating genomic, transcriptomic, and proteomic profiling, offer a more personalized approach to MPN treatment by tailoring interventions to individual mutation patterns. Additionally, novel therapeutic strategies, including gene editing technologies, RNA-based therapies, and nanoparticle-mediated drug delivery systems, hold promise for overcoming current treatment limitations. The integration of artificial intelligence in drug discovery and biomarker identification further enhances the potential for targeted therapies. Future research should focus on refining these strategies, developing reliable biomarkers for patient stratification, and exploring combination therapies that enhance treatment efficacy while minimizing adverse effects. By addressing mitochondrial dysfunction as an underlying driver of MPNs, these emerging approaches have the potential to improve disease management, extend patient survival, and enhance quality of life. Also, this new approach of precision medicine allows patient stratification and ensures that treatments are formed according to the individual disease biology of each patient, which results in overall better outcomes.

combination drug therapy↗

PubMed2025

Vasoactive Agent Therapy in Septic Shock: From Monotherapy Battles to Tailored Hemodynamic Optimization.

Hemodynamic stabilization and preservation of organ perfusion are central elements in the management of septic shock. This is achieved by fluid resuscitation and by administration of vasoactive agents to secure a time window for definitive cause-directed therapy. Guided by the Surviving Sepsis Campaign, the optimization of vasoactive agent strategies, namely protective hemodynamic management, has become a central focus. Tracing key research over the past 25 years reveals a paradigm shift in vasopressor therapy, from empiricism to goal-directed strategy. This evolution has deepened our understanding of the benefit-risk profile of vasoactive agents and fostered a new conceptual framework regarding organ perfusion and protection. Under this framework, management strategies have advanced from the mere pursuit of hemodynamic parameters to care bundles that integrate the monitoring of organ perfusion, microcirculation, and oxygen metabolism. These advances have optimized agent selection, established safe dosing ranges, and ultimately promoted the widespread adoption of combined and multimodal therapy concepts. This review delineates this transformative journey, synthesizing evidence on the reappraisal of traditional agents and exploring "de-catecholaminization" strategies, thereby aiming to broaden the therapeutic landscape. The integration of artificial intelligence and genomic medicine is expected to further advance personalized management strategies for septic shock.

Humans↗

PubMed2001

Colour categorization by domestic chicks.

Spectral stimuli form a physical continuum, which humans divide into discrete non-overlapping regions or categories that are designated by colour names. Little is known about whether non-verbal animals form categories on stimulus continua, but work in psychology and artificial intelligence provides models for stimulus generalization and categorization. We compare predictions of such models to the way poultry chicks (Gallus gallus) generalize to novel stimuli following appetitive training to either one or two colours. If the two training colours are (to human eyes) red and greenish-yellow or green and blue, chicks prefer intermediates, i.e. orange rather than red or yellow and turquoise rather than green or blue. The level of preference for intermediate colours implies that the chicks interpolate between the training stimuli. However, they do not extrapolate beyond the limits set by the training stimuli, at least for red and yellow training colours. Similarly, chicks trained to red and blue generalize to purple, but they do not generalize across grey after training to the complementary colours yellow and blue. These results are consistent with a modified version of a Bayesian model of generalization from multiple examples that was proposed by Shepard and show similarities to human colour categorization.

Animals↗

PubMed1982

Specific impairments of planning.

An information-processing model is outlined that predicts that performance on non-routine tasks can be impaired independently of performance on routine tasks. The model is related to views on frontal lobe functions, particularly those of Luria. Two methods of obtaining more rigorous tests of the model are discussed. One makes use of ideas from artificial intelligence to derive a task heavily loaded on planning abilities. A group of patients with left anterior lesions has a specific deficit on the task. Subsidiary investigations support the inference that this is a planning impairment.

Attention↗

PubMed1997

Movement, activity and action: the role of knowledge in the perception of motion.

This paper presents several approaches to the machine perception of motion and discusses the role and levels of knowledge in each. In particular, different techniques of motion understanding as focusing on one of movement, activity or action are described. Movements are the most atomic primitives, requiring no contextual or sequence knowledge to be recognized; movement is often addressed using either view-invariant or view-specific geometric techniques. Activity refers to sequences of movements or states, where the only real knowledge required is the statistics of the sequence; much of the recent work in gesture understanding falls within this category of motion perception. Finally, actions are larger-scale events, which typically include interaction with the environment and causal relationships; action understanding straddles the grey division between perception and cognition, computer vision and artificial intelligence. These levels are illustrated with examples drawn mostly from the group's work in understanding motion in video imagery. It is argued that the utility of such a division is that it makes explicit the representational competencies and manipulations necessary for perception.

Motion Perception↗

PubMed2026

Architectural logic of the 3D genome: mechanisms of dysregulation and emerging cancer therapeutics.

The three-dimensional (3D) genome provides an essential layer of organization that shapes genome function in space and time. Chromatin compartments and topologically associating domains (TADs) arise from the interplay between intrinsic properties of chromatin and architectural factors, including cohesin and CTCF. Despite substantial progress in defining these structural features, whether 3D genome architecture plays a causal role in regulating processes such as transcription, DNA replication, and DNA repair, or instead reflects underlying regulatory activity, remains unresolved. Here, we use the distinction between chromatin-intrinsic features and architectural factors as a framework to evaluate evidence for causality in genome structure-function relationships. We extend this framework to cancer, where both intrinsic alterations (including noncoding mutations, structural variants, and changes in chromatin state) and architectural factor perturbations (such as mutations in architectural proteins and dysregulation of transcriptional machinery) disrupt genome organization and contribute to disease progression. These findings suggest that alterations in genome structure can, in some contexts, actively reshape oncogenic programs. A major limitation in applying 3D genome insights to cancer biology is the cost and complexity of omics assays. Recent advances in artificial intelligence (AI) and machine learning (ML) enable inference and prediction of 3D genome organization from sequence and epigenomic features, providing insight into the extent to which genome folding is encoded intrinsically versus dynamically regulated in architectural factors. This perspective provides a unified view of how genome structure is established, how it relates to function, and how its disruption contributes to tumorigenesis.

3D genome↗

PubMed2005

Novel specificities emerge by stepwise duplication of functional modules.

A functional module can be defined as a spatially or chemically isolated set of functionally associated components that accomplishes a discrete biological process. Modularity is a key attribute of cellular systems, but the mechanisms that underlie the evolution of functional modules are largely unknown. Duplication of modules has been shown to be an efficient mechanism for the generation of functional innovation in the field of artificial intelligence, but has not been studied in biological networks. Therefore, we ask whether module duplication occurs in cellular networks. We developed a generic framework for the analysis of module duplication, and use it in a large-scale analysis of Saccharomyces cerevisiae protein complexes. Protein complexes are well defined, experimentally derived, functional modules. We observe that at least 6%-20% of the protein complexes have strong similarity to other complexes; thus a considerable fraction has evolved by duplication. Our results indicate that many complexes evolved by step-wise partial duplications. We show that duplicated complexes retain the same overall function, but have different binding specificities and regulation, revealing that duplication of these modules is associated with functional specialization.

Evolution, Molecular↗

PubMed1994

Molecular scene analysis: application of a topological approach to the automated interpretation of protein electron-density maps.

Methods to assist in the spatial and visual analysis of electron-density maps have been investigated as part of a project in molecular scene analysis [Fortier, Castleden, Glasgow, Conklin, Walmsley, Leherte & Allen (1993). Acta Cryst. D49, 168-178]. In particular, the usefulness of the topological approach for the segmentation of medium-resolution (3 A) maps of proteins and their interpretation in terms of structural motifs has been assessed. The approach followed is that proposed by Johnson [Johnson (1977). ORCRIT. The Oak Ridge Critical Point Network Program. Chemistry Division, Oak Ridge National Laboratory, USA] which provides a global representation of the electron-density distribution through the location, identification and linkage of its critical points. In the first part of the study, the topological approach was applied to calculated maps of three proteins of small to medium size so as to develop a methodology that could then be used for analyzing maps of medium resolution. The methodology was then applied to both calculated and experimental maps of penicillopepsin at 3 A resolution. The study shows that the networks of critical points can provide a useful segmentation of the maps, tracing the protein main chains and capturing their conformation. In addition, these networks can be parsed in terms of secondary-structure motifs, through a geometrical analysis of the critical points. The procedure adopted for secondary-structure recognition, which was phrased in terms of geometry-based rules, provides a basis for a further automated implementation of a more complete set of recognition operations through the use of artificial-intelligence techniques.

Journal Article↗

PubMed1992

Initial results for automated computational modeling of patient-specific electromagnetic hyperthermia.

Developments in finite-difference time-domain (FD-TD) computational modeling of Maxwell's equations, super-computer technology, and computed tomography (CT) imagery open the possibility of accurate numerical simulation of electromagnetic (EM) wave interactions with specific, complex, biological tissue structures. One application of this technology is in the area of treatment planning for EM hyperthermia. In this paper, we report the first highly automated CT image segmentation and interpolation scheme applied to model patient-specific EM hyperthermia. This novel system is based on sophisticated tools from the artificial intelligence, computer vision, and computer graphics disciplines. It permits CT-based patient-specific hyperthermia models to be constructed without tedious manual contouring on digitizing pads or CRT screens. The system permits in principle near real-time assistance in hyperthermia treatment planning. We apply this system to interpret actual patient CT data, reconstructing a 3-D model of the human thigh from a collection of 29 serial CT images at 10 mm intervals. Then, using FD-TD, we obtain 2-D and 3-D models of EM hyperthermia of this thigh due to a waveguide applicator. We find that different results are obtained from the 2-D and 3-D models, and conclude that full 3-D tissue models are required for future clinical usage.

Computer Simulation↗