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PubMed2004

Dynamic small animal lung imaging via a postacquisition respiratory gating technique using micro-cone beam computed tomography.

RATIONALE AND OBJECTIVES: Micro computed tomography is an important tool for small animal imaging. On many occasions, it is desirable to image lungs in a live instead of postmortem small animal to perform a pulmonary physiology study. Because the lungs are moving, gating with respect to the ventilatory phase has to be performed to reduce motion artifacts. Precapture ventilation gating may be difficult to achieve in some situations, which motivates us to propose and implement a simple postacquisition gating method. MATERIALS AND METHODS: Rats were used as the subjects in this study. A sequence of low-dose projection images were acquired at 30 frames per second for each view angle. During each capture sequence the rat undergoes multiple ventilation cycles. Using the sequence of projection images, an automated region of interest algorithm, based on integrated grayscale intensity, tracts the ventilatory phase of the lungs. In the processing of an image sequence, multiple projection images are identified at a particular phase and averaged to improve the signal-to-noise ratio. The resulting averaged projection images from different view angles are input to a Feldkamp cone-beam algorithm reconstruction algorithm to obtain isotropic image volumes. RESULTS: Reconstructions with reduced movement artifacts are obtained. In the gated reconstruction, registration of the bone is much better, the edge of the lung is clearly defined, and structures within the lung parenchyma are better resolved. Also, different phases of a breathing cycle can be reconstructed from one single tomographic scan by the proposed gating method. CONCLUSION: A postacquisition gating method using the phase information encoded in the 2-dimensional cone beam projections is proposed. This method is simple to implement and does not require additional experimental set-up to monitor the respiration. It may find applications in lung tumor detection, dynamic pulmonary physiology studies, and the respiratory systems modeling. Minimal motion artifact data sets improve qualitative and quantitative analysis techniques that are useful in physiologic studies of pulmonary structure and function.

Animals↗
PubMed2003

Estimating respiratory system compliance during mechanical ventilation using artificial neural networks.

UNLABELLED: In this study we evaluated whether a technology based on artificial neural networks (ANN) could estimate the static compliance (C(RS)) of the respiratory system, even in the absence of an end-inspiratory pause, during continuous mechanical ventilation. A porcine model of acute lung injury was used to provide recordings of different respiratory mechanics conditions. Each recording consisted of 10 or more consecutive breaths in volume-controlled mechanical ventilation, followed by a breath having an end-inspiratory pause used to calculate C(RS) according to the interrupter technique (IT). The volume-pressure loop of the breath immediately preceding the one with pause was given to the ANN for the training, together with the C(RS) separately calculated by the IT. The prospective phase consisted of giving only the loops to the trained ANN and comparing the results yielded by it to the compliance separately calculated by the investigators. Determination of measurement agreement between ANN and IT methods showed an error of -0.67 +/- 1.52 mL/cm H(2)O (bias +/- SD). We could conclude that ANN, during volume-controlled mechanical ventilation, can extract C(RS) without needing to stop inspiratory flow. IMPLICATIONS: We studied the application of artificial neural networks (ANN) to the estimation of respiratory compliance during mechanical ventilation. The study was performed on an animal model of acute lung injury, testing the performance of ANN in both healthy and diseased conditions of the lung.

Air Pressure↗
PubMed1994

Intelligent Systems for Molecular Biology: review of the first international conference.

This review provides a description of the background to the first international conference on Intelligent Systems for Molecular Biology and a problem-oriented overview of the papers presented. It focuses on genome analysis, gene identification, protein and RNA structure prediction and function, the modelling of biochemical pathways and data and knowledge bases. The range and quality of papers indicates that intelligent systems is emerging as an important sub-field of computational applications in the biological sciences.

Artificial Intelligence↗
PubMed2006

Highly scalable and robust rule learner: performance evaluation and comparison.

Business intelligence and bioinformatics applications increasingly require the mining of datasets consisting of millions of data points, or crafting real-time enterprise-level decision support systems for large corporations and drug companies. In all cases, there needs to be an underlying data mining system, and this mining system must be highly scalable. To this end, we describe a new rule learner called DataSqueezer. The learner belongs to the family of inductive supervised rule extraction algorithms. DataSqueezer is a simple, greedy, rule builder that generates a set of production rules from labeled input data. In spite of its relative simplicity, DataSqueezer is a very effective learner. The rules generated by the algorithm are compact, comprehensible, and have accuracy comparable to rules generated by other state-of-the-art rule extraction algorithms. The main advantages of DataSqueezer are very high efficiency, and missing data resistance. DataSqueezer exhibits log-linear asymptotic complexity with the number of training examples, and it is faster than other state-of-the-art rule learners. The learner is also robust to large quantities of missing data, as verified by extensive experimental comparison with the other learners. DataSqueezer is thus well suited to modern data mining and business intelligence tasks, which commonly involve huge datasets with a large fraction of missing data.

Algorithms↗
PubMed2002

Competence Center for Miniaturised Monitoring and Intervention Systems MOTIV.

In the national Competence Center, the whole value chain, from the idea to the development of a product ready for the market, is brought together. MOTIV has underlined three focus point: It wants to improve the therapy and therapy control, to develop intelligent microimplants and to create innovative telematic home healthcare concepts.

Academies and Institutes↗
PubMed2001

GenePath: a computer program for genetic pathway discovery from mutant data.

The sequencing of the human genome and the genomes of several model organisms is the first step toward the long-term objective of genetic research: the identification of all genes, and the discovery of their functions and mutual interactions. This article presents a methodology and a computer program called GenePath to support the discovery of gene function. GenePath uses mutant data and available genetic knowledge to identify potential genetic pathways. Several pilot applications based on experimental results from Dictyostelium and C. elegans confirmed the usefulness of the proposed schema. Our results suggest that GenePath is a valuable tool that can be used as an intelligent assistant to support genetic reasoning.

Animals↗
PubMed1994

Preliminary evaluation of an intelligent system for the management of labour.

Over the past 10-15 years, workers using conventional computing approaches have attempted to provide an accurate assessment of fetal condition during labour based on the cardiotocogram (CTG) alone. These have not proved successful perhaps because the correct interpretation of fetal condition also requires physiological knowledge, specific patient information, knowledge of events during labour and considerable practical experience. An intelligent system which considers all the relevant information and embodies expertise may better diagnose fetal condition and support decision making. This study reports the preliminary evaluation of such a system and investigates whether this approach can attain a performance comparable with experienced local clinicians. From a database of 200 high risk labour records, 30 cases were selected; the 9 cases which received clinical intervention for 'fetal-distress' and a further 21 cases selected randomly. The management specified by the system, 3 experienced clinicians (A, B and C) and the actual clinical management were compared in a retrospective blinded review. The experts were found to agree well with each other. Expert A reviewed the cases five months later and was found to be entirely consistent in the management of 28 of the 30 cases. The system's actions were indistinguishable from the experts' and in no case did the system recommend an action not also recommended by at least one experienced reviewer. This study demonstrates the potential of an intelligent system to assist in the management of labour.

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↗
PubMedDate not supplied

Analysis of respiratory pressure-volume curves in intensive care medicine using inductive machine learning.

We present a case study of machine learning and data mining in intensive care medicine. In the study, we compared different methods of measuring pressure-volume curves in artificially ventilated patients suffering from the adult respiratory distress syndrome (ARDS). Our aim was to show that inductive machine learning can be used to gain insights into differences and similarities among these methods. We defined two tasks: the first one was to recognize the measurement method producing a given pressure-volume curve. This was defined as the task of classifying pressure-volume curves (the classes being the measurement methods). The second was to model the curves themselves, that is, to predict the volume given the pressure, the measurement method and the patient data. Clearly, this can be defined as a regression task. For these two tasks, we applied C5.0 and CUBIST, two inductive machine learning tools, respectively. Apart from medical findings regarding the characteristics of the measurement methods, we found some evidence showing the value of an abstract representation for classifying curves: normalization and high-level descriptors from curve fitting played a crucial role in obtaining reasonably accurate models. Another useful feature of algorithms for inductive machine learning is the possibility of incorporating background knowledge. In our study, the incorporation of patient data helped to improve regression results dramatically, which might open the door for the individual respiratory treatment of patients in the future.

Adult↗
PubMed2026

AI-Driven Precision Medicine in Alzheimer's Disease: Drug Repurposing, Digital Therapeutics and Clinical Decision Support.

Alzheimer's Disease (AD) is a neurodegenerative disease that causes significant clinical, social, and economic burden worldwide. Despite improvements in understanding its multifaceted pathogenesis, current treatments are mostly symptomatic and ineffective across varied patient populations. To overcome these constraints, AI-driven precision medicine allows tailored risk assessment, treatment selection, and disease monitoring. This review covers AI's role in AD precision medicine, focusing on drug repurposing, digital therapies and clinical decision support systems. Machine and deep learning models are used to predict medication response, integrate heterogeneous data sources such as genomics, transcriptomics, neuroimaging and electronic health records, and uncover pharmacogenomic treatment success factors. The paper covers AIenabled precision pharmacology, including tailored dosing algorithms, adaptive therapeutic monitoring, and adverse drug reaction prediction. Bioinformatics-based target identification, network pharmacology, graphbased AI models, virtual screening, and real-world and clinical data validation are emphasized in AI-driven medication repurposing. AI-powered digital treatments like personalized cognitive training platforms, wearable- derived digital biomarkers, virtual and mixed reality interventions, adherence monitoring, and digital twins for therapy optimization have been discussed. AI-based clinical decision support systems are also thoroughly assessed for clinical value, accuracy, and explainability in disease subtyping, trajectory prediction, and risk stratification in preclinical and prodromal AD. Despite these promises, data heterogeneity, algorithmic bias, legal barriers, and privacy concerns exist. Federated learning enables safe multi-center collaboration and hybrid AI-human approaches, and it represents the future. AI's ability to alter AD care opens the door to precision medicine paradigms that use repurposed medications, digital tools and intelligent decision-making to improve patient outcomes.

Alzheimer’s disease↗
PubMed1993

A service-oriented information sources database for the biological sciences.

Researchers in the biological sciences require access to a variety of information sources located in various places on different computer networks. In order to satisfy the information needs of a researcher, appropriate information sources must be selected and access to these information sources and the computing services supporting them must be provided in a way that does not distract the researcher from problems of real interest. At the University of Missouri-Columbia a service-oriented information sources database is being developed as a key component of a layered-model design of an intelligent system which will provide a research environment appropriate to the needs of researchers in the biological sciences.

Artificial Intelligence↗
PubMed2004

Neural networks with periodogram and autoregressive spectral analysis methods in detection of epileptic seizure.

Approximately 1% of the people in the world suffer from epilepsy. Careful analyses of the electroencephalograph (EEG) records can provide valuable insight and improved understanding of the mechanisms causing epileptic disorders. Predicting the onset of epileptic seizure is an important and difficult biomedical problem, which has attracted substantial attention of the intelligent computing community over the past two decades. The purpose of this work was to investigate the performance of the periodogram and autoregressive (AR) power spectrum methods to extract classifiable features from human electroencephalogram (EEG) by using artificial neural networks (ANN). The feedforward ANN system was trained and tested with the backpropagation algorithm using a large data set of exemplars. We present a method for the automatic comparison of epileptic seizures in EEG, allowing the grouping of seizures having similar overall patterns. Each channel of the EEG is first broken down into segments having relatively stationary characteristics. Features are then calculated for each segment, and all segments of all channels of the seizures of a patient are grouped into clusters of similar morphology. This clustering allows labeling of every EEG segment. Examples from 5 patients with scalp electrodes illustrate the ability of the method to group seizures of similar morphology. It was observed that ANN classification of EEG signals with AR preprocessing gives better results, and these results can also be used for the deduction of epileptic seizure.

Algorithms↗
PubMed2002

MIMIC II: a massive temporal ICU patient database to support research in intelligent patient monitoring.

Development and evaluation of Intensive Care Unit (ICU) decision-support systems would be greatly facilitated by the availability of a large-scale ICU patient database. Following our previous efforts with the MIMIC (Multi-parameter Intelligent Monitoring for Intensive Care) Database, we have leveraged advances in networking and storage technologies to develop a far more massive temporal database, MIMIC II. MIMIC II is an ongoing effort: data is continuously and prospectively archived from all ICU patients in our hospital. MIMIC II now consists of over 800 ICU patient records including over 120 gigabytes of data and is growing. A customized archiving system was used to store continuously up to four waveforms and 30 different parameters from ICU patient monitors. An integrated user-friendly relational database was developed for browsing of patients' clinical information (lab results, fluid balance, medications, nurses' progress notes). Based upon its unprecedented size and scope, MIMIC II will prove to be an important resource for intelligent patient monitoring research, and will support efforts in medical data mining and knowledge-discovery.

Artificial Intelligence↗
PubMed1995

Acts and knowledge management in an open hospital information system.

This communication presents the management of a customizable patient medical dossier as implemented in the NUCLEUS project. After a brief reminder of the NUCLEUS hospital information system features, we discuss the two main innovative concepts underlying the intelligent management of the integrated patient dossier: acts management and knowledge management. The functionalities related to patient dossier are then introduced.

Artificial Intelligence↗
PubMed1996

Evaluation of a knowledge-based system providing ventilatory management and decision for extubation.

We evaluated whether a knowledge-based system (KBS) connected to a ventilator in pressure support mode could correctly predict the ability of patients to tolerate total withdrawal from ventilatory support. The KBS was designed to continuously adapt ventilatory assistance to the needs of the patient, to manage a strategy of gradually decreasing ventilatory assistance, and to indicate when the patient was able to breathe without assistance. Thirty-eight patients for whom weaning was being considered were evaluated using a conventional battery of parameters, including weaning criteria, tolerance of a T-piece trial, and outcome 48h after permanent withdrawal of ventilation. The results of this evaluation were compared with the suggestions made by the KBS at the end of a period of KBS-driven mechanical ventilation inserted in the conventional weaning procedure. The positive predictive value of the KBS was 89%, versus 77% for the conventional procedure and 81% for the rapid shallow breathing index alone. The KBS correctly predicted the course of five patients who tolerated a T-piece trial but required ventilation within 48 h. We conclude that our KBS ensured appropriate patient management during the weaning period and improved our ability to predict responses to weaning.

Adult↗
PubMed2005

[Elemental research on intelligent non-invasive temporary pacemakers].

Some research on intelligent non-invasive temporary pacemakers is introduced in this paper. An industrial computer, some IC chips and other elements are used to construct its hardware, and its software is in C++ language. The experimental device has some intelligent functions of recognizing some arrhythmia. The system has a pacemaker module and an ECG monitor module. Its software includes a main program, a RS-232C communication program, a printer VxD, a pacing control VxD and ECG signal pretreatment and recognizing program and so on. The pacing-generating circuit is employed to make the precision control of pacing current. The communication between industrial-computer system and ECG module is completed through the DLL. The real time processing of ECG signals is based on filter method for a higher recognizing ratio. The system calculates several parameters to recognize certain arrhythmia and uses MIT/BIH database to validate the reliability of ECG recognition.

Algorithms↗
PubMed2003

A knowledge-based approach to the deflocculation problem: integrating on-line, off-line, and heuristic information.

A knowledge-based approach for the supervision of the deflocculation problem in activated sludge processes was considered and successfully applied to a full-scale plant. To do that, a methodology that integrates on-line, off-line and heuristic information has been proposed. This methodology consists of three steps: (i). development of a decision tree (which involves knowledge acquisition and representation); (ii). implementation into a rule-based system; and (iii). validation. The set of symptoms most useful in diagnosing the deflocculation problem has been identified, the different branches to diagnose pin-point floc and dispersed growth have been built (using generic and specific knowledge), and all this knowledge has been codified into an object-oriented shell. The results obtained in the application of this knowledge-based approach to the Granollers WWTP (which treats about 130000 inhabitants-equivalents) showed that the system was able to identify correctly the problem with reasonable accuracy. Our positive experience building this system suggests that this approach is a practical and valuable element to include in an intelligent supervisory system combining numerical and reasoning techniques.

Artificial Intelligence↗