Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Intelligent Systems”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 181 records · Page 10Linked to original sources
PubMed2026

Artificial Intelligence in Predicting Systemic Complications From Retinal Findings: A New Frontier in Precision Medicine.

Innovations in retinal imaging technologies and growing evidence from retinal imaging of systemic and neurodegenerative diseases have begun to explore the utility of retinal imaging in diagnosing these conditions. Since the retina shares embryological origins with the central nervous system and reflects systemic microvascular characteristics, it is well positioned for noninvasive observation of patients' systemic and neural health. Moreover, accessibility of retinal imaging has improved with the increasing number of ophthalmology clinics. Rapid improvements in various deep learning (DL) tools have also catalyzed the automation of retinal imaging analysis. Systems that utilize DL for retinal imaging are being developed to assist with disease recognition, clinical judgment, and prognostic assessment of systemic health. Various imaging modalities are being integrated with existing genomic and clinical data to estimate an individual's predisposition to certain conditions. Contrary to many existing reviews, the objective of this review is to synthesize the most recent clinical and technological evidence on DL-based diagnostic systems for retinal imaging, with a focus on how different network architectures and their combinations have been developed, validated, and applied across systemic disease detection and prediction. Specifically, this review examines the datasets, model validation approaches, and automated diagnostic systems reported in recent literature. It discusses the extent to which these advancements address existing barriers toward real-time diagnostic application across clinical disciplines. Integrating retinal imaging with DL is an innovative and promising approach to precision medicine and health risk reduction.

artificial intelligence↗
PubMed1996

A virtual reality interface to an intelligent dental care system.

The design and fabrication of teeth restorations in dentistry rely increasingly on CAD/CAM techniques. We present an approach for interactive design of the occlusal surface of teeth based on simulation of jaw articulation and computer-aided diagnosis of occlusal disorders. To bridge the cognitive gap between the dentist and the computer system, we propose a virtual reality user interface, which applies the metaphors of tools and techniques known in dentistry. This makes the restoration design more intuitive for dentists. The system uses Virtual Reality Modeling Language (VRML) and HTML standards to generate a treatment report and exchange data in an electronic form. The simulation of jaw articulation requires fast calculation of multi-point contacts and detection of collisions between surfaces of teeth and restorations. We have developed a distance maps technique which exhibits realtime performance for objects with complex geometry and is suitable for other virtual reality systems dealing with complex contacts. The characteristics of contacts between teeth acquired during lower jaw motion are compactly represented as accumulated distance maps. These maps are then used for automatic removal of interferences between the restorations and the opponent teeth, and provide the dentist with information for further manual adjustments of the occlusal surfaces.

Artificial Intelligence↗
PubMed1997

IDIS-KS: an intelligent drug information system as a knowledge server.

Expert System technology in combination with other technologies such as Networks and Data Base systems can prove to be a valuable tool for medical experts, providing decision support and information services, and therefore facilitating and improving their everyday tasks. IDIS-KS described in this paper, is an consultation and information system dedicated to deliver drug information and suggestions about possible treatments to medical practitioners in the National area of Greece.

Computer Communication Networks↗
PubMed1985

Office management: increasing productivity in the workplace.

Healthcare managers are seeking new ways to increase productivity in the workplace. New technologies, such as word processing, electronic mail, electronic conferencing, calendar management, data base management systems, artificial intelligence, decision support systems, and local area networks, are being developed to help the healthcare manager make the workplace more productive. The challenge therefore is to implement these systems smoothly and address the organizational issues associated with implementation.

Decision Making↗
PubMed1990

Cancer chronotherapy: a drug delivery challenge.

The toxicity and/or efficacy of more than twenty anticancer agents have been shown in various experimental systems to be dependent upon the circadian timing of their bolus administration or the circadian shaping of their continuous infusion. In cancer patients, the toxicity of several single agents, given either as bolus or infusion, and a growing number of drug combinations have been shown to similarly depend upon their timing. While clinical trials currently underway demonstrate that the circadian stage of drug toxicity and dose intensity each depend upon their circadian timing, definitive investigations of whether or not cancer control and patient survival are similarly dependent upon circadian treatment timing are currently under way. Both clinical trials of treatment timing and chronotherapy depend totally upon the development and use of programmable wearable and implantable, single-channel and multi-channel, open and eventually closed loop delivery systems. First generation intelligent delivery systems are currently available, work well, are economical and are destined, for economic reasons, to be more widely used. When used, each system requires temporal input, making it impossible to avoid specification of drug sequence, interval between drugs or treatment cycles and circadian treatment timing. The advent of biological therapy with cytokines and growth factors makes it likely that the precise timing of cancer therapies will be of growing importance.

Animals↗
PubMedDate not supplied

Automated anesthesia data management and recordkeeping.

It is apparent that judicious application of computer technology to the design and implementation of an automated anesthesia recordkeeping system could afford increased ease of use to the anesthesiologist compared to a manually kept record. Although prototype systems have been developed at academic institutions, and commercially available operating room physiological monitoring systems show increasing capability for some recordkeeping functions, production of an effective AAD-MARKS will depend on the development of suitable display formats and capabilities, markedly improved user interfaces for data input and system control, intelligent graduated alarm systems, and demonstrated reliability, with provision for preservation of critical data and recordkeeping functions and basic physiological data monitoring despite system failure.

Anesthesia↗
PubMed1995

Toward an intelligent wound assessment system.

There is general agreement regarding the need for pressure ulcer assessment methodology which more discretely reflects relevant aspects of wound status than does the commonly used staging system. The Pressure Sore Status Tool (PSST) is one such instrument which was developed with consensual expert input. While the psychometric properties of the PSST have been reported in the literature, the instrument was validated using ET nurses, highly trained wound care specialists, and existed only in manual form. This paper reports results from attempts to establish reliability estimates for healthcare practitioners without extraordinary wound care training or experience. The paper further describes the automation of the PSST and provides examples of pressure ulcer profiles tracked over time. Results indicate that inter-rater reliability with general healthcare practitioners was .78 and intra-rater reliability was .89. The practitioners were able to use the PSST for over six months and the automated system allowed analysis of wound healing profiles that would have been difficult using a manual system. These results imply that movement toward an automated system which makes discriminations regarding the effects of various treatment and intervention strategies is possible and practical.

Aged↗
PubMedDate not supplied

The goal of PACS in Nagoya University Hospital.

In Nagoya University Hospital, a Radiology Intelligent Information System (RIIS) is under construction which will be linked with the Hospital Intelligent Information System (HIIS). RIIS is composed of the radiation oncology information system and the diagnostic radiology information system which is named Imaging Diagnosis Intelligent Information System (IDIIS). IDIIS consists of three parts: (a) the Imaging Diagnosis Management System (IDMS); (b) the Picture Archiving and Communication System (PACS); (c) the Report Generation Support System for Imaging Diagnosis (RGSS-ID). Artificial intelligence methodology is applied to RGSS-ID and IDMS which includes the ordering and scheduling system of diagnostic imaging. IDIIS has an important role to improve the quality of patient care and medical education as well as image management and is an essential component for the implementation of HIIS.

Computer Systems↗
PubMed1991

Computerized measurement of speech intelligibility. I. Development of system and procedures.

An experimental system for the measurement of speech intelligibility has been developed. It uses a Personal Computer (PC), together with appropriate software to handle playback of test words in carrier phrases, presented in a 'closed response' test condition. Information about the intelligibility, based on the correct responses and the confusions, is immediately available due to simultaneous collecting and sorting of subjects' responses. The system works satisfactorily and reliably and has been well received by experimenters as well as by adult test subjects in the age range 18-70 years. From a new Danish standard speech material for audiological purposes, a Multiple Choice speech intelligibility test has been devised. The test is called 4AFC (Four Alternative Forced Choice) and is based on monosyllabic words with consonant confusions. Normative data for the 4AFC test, obtained with the computerized system, are presented in an accompanying paper.

Adolescent↗
PubMed1994

Artificial intelligence in radiology: decision support systems.

Computer-based systems that incorporate artificial intelligence techniques can help physicians make decisions about their patients' care. In radiology, systems have been developed to help physicians choose appropriate radiologic procedures and to formulate accurate diagnoses. These decision support systems use techniques such as rule-based reasoning, artificial neural networks, hypertext, Bayesian networks, and case-based reasoning. This article reviews these artificial intelligence techniques, describes their application in radiology, and discusses the role that decision support systems may play in radiology's future.

Artificial Intelligence↗
PubMed1992

[Intelligent instrumentation in medicine].

The introduction of intelligent robots, expert systems and other forms of intelligent automatization in the current practice of medicine seems to be inevitable. It appears interesting to look back to the efforts that have been done, since the former steps, about three decades ago and consider the prospects in this field for both short and long term. Simultaneously it is interesting to reckon the new aspects which are raised with the evolution of these methodologies such as the responsibility of decisions taken by intelligent systems, the probable advantages, at the present stage, of the interactive systems and the risk of self-learning systems. Some efforts carried out in our department in this field are described.

Artificial Intelligence↗
PubMed1988

Computer-assisted breast cancer grading.

Morphological tumour differentiation has been shown in numerous studies to give a good prognosis in breast cancer, but as histological grading is based upon a subjective assessment of microscopical appearances, difficulties in consistency and reproducibility are inevitable. A review of the many conventional methods served to highlight a common limitation in their approach; lack of structure. We introduce a new approach which seeks to overcome the problem, by formalizing the methods and identifying aspects which are well suited to computer aided analysis, these being incorporated into a microcomputer system facilitating the collection and appraisal of morphometric data. Within the Information Technology Institute (ITRI) at Brighton Polytechnic a research team is carrying out multidisciplinary work into the elucidation of biological systems. This programme, entitled 'Intelligent Medical Systems', used methods of mathematical signal processing and artificial intelligence, applied to a number of areas, one of which is described in this paper. The aim has been to utilize the inherent skill exercised by the histopathologist in interpreting microscopical images, whilst making quantitization more accurate and reproducible. the system has been developed within a highly structured framework and will have applications in teaching and routine histological analysis. The value of artificial intelligence techniques in the wider issues of this area is discussed.

Artificial Intelligence↗
PubMed1996

A computerized diagnostic system for the interpretation of umbilical artery blood flow velocity waveforms.

OBJECTIVE: Development of an artificial intelligent diagnostic system for the interpretation of umbilical artery blood flow velocity waveform measurements. STUDY DESIGN: Study design comprised several stages including data acquisition, image processing and analysis, training of artificial neural network and testing the predictive value of the system. The clinical material was handled in two groups. The training group consisted of 952 umbilical artery blood flow velocity waveform images of 174 normal pregnancies with normal outcome, while the testing group was composed of 138 images derived from 20 normal pregnancies with normal outcome and 68 images of 16 high risk pregnancies with poor outcome. All subjects were evaluated by Doppler ultrasonography and umbilical artery blood flow velocity waveform images were transferred to the computer environment by means of a special data acquisition system. Automated image processing and analysis were performed to derive indices such as A/B ratio, resistance index, pulse related index, area ratio of wave and angle of coincident slopes. We have used a supervised artificial neural network (back propagation learning algorithm) to develop an intelligent diagnostic system which is called the BOLU system. RESULTS: This version of the system was trained with the umbilical artery blood flow velocity waveform images of normal pregnancies. Thus, the BOLU system decides whether the tested image is normal for a given gestational week or not. The specificity and sensitivity of this system were estimated to be 98.6% and 51.5% respectively. CONCLUSION: We have developed an artificial intelligent diagnostic system for the interpretation of umbilical artery blood flow velocity waveform measurements. Waveform indices were obtained automatically by image processing and analysis. The predictive value of the system was found to be satisfactory.

Blood Flow Velocity↗
PubMed1997

Neural networks in neurotologic expert systems.

Artificial intelligence donates new possibilities to neurotologic research. Neural networks are a computer-based reasoning method which can be applied in expert systems created for clinical decision support. Neural networks have been used in medical imaging, in medical signal processing and to analyze both clinical and laboratory data. Principally, neural networks simulate the function of the brain. They have to be taught to make correct decisions from the input data. This learning process can be either supervised or unsupervised. The decision making is based on mathematical transformations and it occurs on a hidden level. Calculations are made on parallel manner and the decision making simulates pattern recognition method. Neural networks suit well in medical problems which cannot be defined in simple rules. A drawback of neural networks is that the decisions are irrational and cannot be motivated to the user. Another problem is neural networks' difficulty to handle incomplete input data, i.e., how to define some default or expected values for unknown input parameters. In a complex medical area, which would require multilayered neural networks, the neural networks require a large amount of solved cases for the learning process. In our experience neural networks seem not suitable for diagnosing vertigo and a better choice would be either case-based reasoning or possibly genetic algorithms or a combination of these.

Diagnosis, Computer-Assisted↗