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At least 451 records · Page 25Linked to original sources

PubMed2004

Neural network analysis of anal sphincter repair.

PURPOSE: Prediction of success after anterior sphincter repair for incontinence is difficult. Standard multivariate analysis techniques have only 75 to 80 percent accuracy. Artificial intelligence, including artificial neural networks, has been used in the analysis of complex clinical data and has proved to be successful in predicting the outcome of other surgical procedures. Using a neural network algorithm, we have assessed the probability of success after anterior sphincter repair. METHODS: Prospective anorectal physiology data of 72 patients undergoing anterior sphincter repair was collected between 1995 and 1999. Complete data sets of 75 percent of the series were used to train an artificial neural network; the remaining 25 percent were used for data validation. The output was continence grading, ranging from 0 to 4 (worse to continent). RESULTS: The outcome at 3, 6, and 12 months postoperatively was obtained and assessed. The best correlation between actual data value and artificial neural network value was found at 12 months (r = 0.931; P = 0.0001). Clear correlations also were found at three months (r = 0.898; P = 0.0001) and six months (r = 0.742; P = 0.002). Results of applying a net to details excluding pudendal nerve latency were poor. CONCLUSIONS: Artificial neural networks are more accurate (93 percent correlation) than standard statistics (75 percent) when applied to the prediction of outcome after anterior sphincter repair. This assessment also confirms the usefulness of pudendal latency in the prediction of anterior sphincter repair outcome. The results obtained highlight the obvious usefulness of artificial neural networks, which could now be used in a prospective evaluation for application of the technique.

Anal Canal↗

PubMed1999

[New concepts of monitoring and assessment of bone union stiffness in fractures treated by Dynastab DK external fixation].

This paper focuses on heuristic techniques based on artificial intelligence namely artificial non-linear and multilayer neurons employed for assessment of bone union in fractures treated by Dynastab DK external fixation. The software used allowed for prediction of bone union curve at the early stage of treatment. The training of the neural net has been done on the ground of 56 cases of Dynastab DK treated fractures. The net served for periodical assessment of bone union and its further prediction in 17 long bone shaft fractures. Bone mechanical properties at the fracture site showed non-linear changes in time. Specifically great changes were noted 4 months postoperatively. Flexion at the fracture site is critical. Neuron net proved to be useful in predicting bone union curve.

Bone Regeneration↗

PubMed1991

A model for designing intelligent tutoring systems.

A model for the design of an intelligent tutoring system is presented using artificial intelligence techniques and cognitive processing theories. The model of cognitive processing known as the Knowledge Acquisition and Recall Theory is derived from Anderson's Adaptive Control of Thought Theory. The model is used as a basis for a methodology for the design of an intelligent tutoring system that teaches problem-solving strategies for blood grouping discrepancies. The system developed was tested to determine the effectiveness of the methodology developed and to provide support for the concepts in the model of cognition developed. The preliminary results provide some evidence that an intelligent tutoring system designed using the methodology developed may aid in the knowledge acquisition process. (knowledge acquisition, artificial intelligence, computer-assisted instruction, cognition, intelligent tutoring system).

Artificial Intelligence↗

PubMed1994

Application of artifical intelligence principles to the analysis of "crazy" speech.

Artificial intelligence computer simulation methods can be used to investigate psychotic or "crazy" speech. Here, symbolic reasoning algorithms establish semantic networks that schematize speech. These semantic networks consist of two main structures: case frames and object taxonomies. Node-based reasoning rules apply to object taxonomies and pathway-based reasoning rules apply to case frames. Normal listeners may recognize speech as "crazy talk" based on violations of node- and pathway-based reasoning rules. In this article, three separate segments of schizophrenic speech illustrate violations of these rules. This artificial intelligence approach is compared and contrasted with other neurolinguistic approaches and is discussed as a conceptual link between neurobiological and psychodynamic understandings of psychopathology.

Artificial Intelligence↗

PubMed2006

A novel two-dimensional echocardiographic image analysis system using artificial intelligence-learned pattern recognition for rapid automated ejection fraction.

OBJECTIVES: We sought to test the hypothesis that a novel 2-dimensional echocardiographic image analysis system using artificial intelligence-learned pattern recognition can rapidly and reproducibly calculate ejection fraction (EF). BACKGROUND: Echocardiographic EF by manual tracing is time consuming, and visual assessment is inherently subjective. METHODS: We studied 218 patients (72 female), including 165 with abnormal left ventricular (LV) function. Auto EF incorporated a database trained on >10,000 human EF tracings to automatically locate and track the LV endocardium from routine grayscale digital cineloops and calculate EF in 15 s. Auto EF results were independently compared with manually traced biplane Simpson's rule, visual EF, and magnetic resonance imaging (MRI) in a subset. RESULTS: Auto EF was possible in 200 (92%) of consecutive patients, of which 77% were completely automated and 23% required manual editing. Auto EF correlated well with manual EF (r = 0.98; 6% limits of agreement) and required less time per patient (48 +/- 26 s vs. 102 +/- 21 s; p < 0.01). Auto EF correlated well with visual EF by expert readers (r = 0.96; p < 0.001), but interobserver variability was greater (3.4 +/- 2.9% vs. 9.8 +/- 5.7%, respectively; p < 0.001). Visual EF was less accurate by novice readers (r = 0.82; 19% limits of agreement) and improved with trainee-operated Auto EF (r = 0.96; 7% limits of agreement). Auto EF also correlated with MRI EF (n = 21) (r = 0.95; 12% limits of agreement), but underestimated absolute volumes (r = 0.95; bias of -36 +/- 27 ml overall). CONCLUSIONS: Auto EF can automatically calculate EF similarly to results by manual biplane Simpson's rule and MRI, with less variability than visual EF, and has clinical potential.

Aged↗

PubMed2003

Artificial neural networks for decision-making in urologic oncology.

The authors are presenting a thorough introduction in Artificial Neural Networks (ANNs) and their contribution to modern Urologic Oncology. The article covers a description of Artificial Neural Network methodology and points out the differences of Artificial Intelligence to traditional statistic models in terms of serving patients and clinicians, in a different way than current statistical analysis. Since Artificial Intelligence is not yet fully understood by many practicing clinicians, the authors have reviewed a careful selection of articles in order to explore the clinical benefit of Artificial Intelligence applications in modern Urology questions and decision-making. The data are from real patients and reflect attempts to achieve more accurate diagnosis and prognosis, especially in prostate cancer that stands as a good example of difficult decision-making in everyday practice. Experience from current use of Artificial Intelligence is also being discussed, and the authors address future developments as well as potential problems such as medical record quality, precautions in using ANNs or resistance to system use, in an attempt to point out future demands and the need for common standards. The authors conclude that both methods should continue to be used in a complementary manner. ANNs still do not prove always better as to replace standard statistical analysis as the method of choice in interpreting medical data.

Humans↗

PubMed1988

Knowledge-based educational systems.

In knowledge-based educational systems, the key concept is that information and procedures are represented in the same data structure. These structures can search for each other in flexible and, consequently, very robust ways. At the Air Force Human Resources Laboratory (AFHRL), our researchers are building computer environments that know what they know, know how people can best use them, and know how to draw inferences about their state--self-referential electronic tutors. In September 1986, artificial intelligence researchers participated in AFHRL's Research Planning Forum for Intelligent Tutorial Systems (ITS). This essay reviews the state of the philosophy, art, and science of artificial intelligence (AI) approaches to education. Then it summarizes the research issues which were presented, discussed, and better defined in this Forum--namely the nature and representation of 1) expertise modules, 2) student diagnostic modules, 3) adaptive instructional and curriculum modules, 4) instructional environments, and 5) man-machine interfaces. Advances in artificial intelligence, cognitive science, and instructional discourse have provided a means for investigating human learning, for representing an individual's own "knowledge processing." Research and development in knowledge-based educational systems seems promising, not only for helping people learn how to perform complex tasks, but also for explicitly expressing how people learn to learn. Therefore, would it not be wise to establish a scientific legacy for the development of effective knowledge-based tutorial systems which is informed by the best studies of mind and meaning, language and thought, purpose and paradox?

Artificial Intelligence↗

PubMedDate not supplied

Decision support for tendon tissue engineering.

The aim of this study is to provide decision support with artificial intelligence for tendon tissue engineering strategies. The experimental data of tissue-engineered tendons were integrated and standardized with a centralized database, and a decision support system was developed using both artificial neural networks and decision trees. The decision support system was trained with existing cases in the database, and then was used to generate tissue engineering schemes for new experimental animals. Following the schemes generated by the artificial intelligent system, we cured 28 of the 30 experimental animals. In conclusion, artificial intelligence is a powerful method for decision support in the tendon tissue engineering realm.

Animals↗

PubMed2025

Improving Community-Based Care for Adolescents with ADHD: a Randomized Controlled Trial of Artificial Intelligence-Assisted Fidelity Supports.

Cognitive-behavioral treatments (CBTs) for adolescents with ADHD demonstrate promise of long-term effects on outcome. However, their implementation in routine care community clinics faces barriers that impact quantity, efficiency, and quality of delivery, as well as client outcomes. This study is a randomized controlled trial designed to evaluate the impact of an AI-assisted service delivery model on therapist implementation of Supporting Teens' Autonomy Daily (STAND), a CBT blended with Motivational Interviewing (MI) for adolescents with ADHD. Adolescents with ADHD (N = 51), who were clients at three community mental health agencies, received treatment from 23 therapists. There was randomization of adolescents and therapists to AI-assisted or standard implementation supports. In addition to standard supports (i.e., training, standard facilitation resources, technical assistance, case supervision), AI-assisted support package included digitized facilitation resources housed in a clinical dashboard (Care4), feedback on content fidelity, and AI-generated feedback on MI implementation quality. The AI-assisted group was associated with more efficient treatment delivery and lower number of appointments attended by the adolescent. There was also a significant decrement in MI quality over time in the AI-assisted group compared to the standard support group. Feedback in focus groups indicated that therapists perceived a task-oriented mindset to be associated with receipt of the AI-assisted support package, leading therapists to prioritize efficiency over relational aspects of therapy. Following the results of this trial, a future, larger RCT should examine the impact of the AI-assisted implementation model on mental health outcomes and cost savings to organizations, third party payers, and clients. Trial registration number: NCT05135065; https://www.clinicaltrials.gov ; Registered September 2021.

Humans↗

PubMed1986

Toward automated image analysis: future possibilities in historical perspective.

This field is in its infancy. Some people look at artificial intelligence and just call it complex programming, but artificial intelligence is now being applied to image understanding and attempts to utilize knowledge about the domain of the image to analyze it. The practicality of this approach is a long way off, however, and the radiologist of today does not need to fear that computers will change the work habits of image interpretation for some time to come.

Artificial Intelligence↗

PubMed1998

AI in medical education--another grand challenge for medical informatics.

The potential benefits of artificial intelligence in medicine (AIM) were never realized as anticipated. This paper addresses ways in which such potential can be achieved. Recent discussions of this topic have proposed a stronger integration between AIM applications and health information systems, and emphasize computer guidelines to support the new health care paradigms of evidence-based medicine and cost-effectiveness. These proposals, however, promote the initial definition of AIM applications as being AI systems that can perform or aid in diagnoses. We challenge this traditional philosophy of AIM and propose a new approach aiming at empowering health care workers to become independent self-sufficient problem solvers and decision makers. Our philosophy is based on findings from a review of empirical research that examines the relationship between the health care personnel's level of knowledge and skills, their job satisfaction, and the quality of the health care they provide. This review supports addressing the quality of health care by empowering health care workers to reach their full potential. As an aid in this empowerment process we argue for reviving a long forgotten AIM research area, namely, AI based applications for medical education and training. There is a growing body of research in artificial intelligence in education that demonstrates that the use of artificial intelligence can enhance learning in numerous domains. By examining the strengths of these educational applications and the results from previous AIM research we derive a framework for empowering medical personnel and consequently raising the quality of health care through the use of advanced AI based technology.

Artificial Intelligence↗

PubMed2025

Design of highly functional genome editors by modelling CRISPR-Cas sequences.

Gene editing has the potential to solve fundamental challenges in agriculture, biotechnology and human health. CRISPR-based gene editors derived from microorganisms, although powerful, often show notable functional tradeoffs when ported into non-native environments, such as human cells1. Artificial-intelligence-enabled design provides a powerful alternative with the potential to bypass evolutionary constraints and generate editors with optimal properties. Here, using large language models2 trained on biological diversity at scale, we demonstrate successful precision editing of the human genome with a programmable gene editor designed with artificial intelligence. To achieve this goal, we curated a dataset of more than 1 million CRISPR operons through systematic mining of 26 terabases of assembled genomes and metagenomes. We demonstrate the capacity of our models by generating 4.8× the number of protein clusters across CRISPR-Cas families found in nature and tailoring single-guide RNA sequences for Cas9-like effector proteins. Several of the generated gene editors show comparable or improved activity and specificity relative to SpCas9, the prototypical gene editing effector, while being 400 mutations away in sequence. Finally, we demonstrate that an artificial-intelligence-generated gene editor, denoted as OpenCRISPR-1, exhibits compatibility with base editing. We release OpenCRISPR-1 to facilitate broad, ethical use across research and commercial applications.

CRISPR-Cas Systems↗

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↗

PubMed1991

Intelligent systems: how can they help?

Expert systems are a branch of the computer science of artificial intelligence. Their ability to mimic experts by applying their domain knowledge has led to the construction of a number of medical applications. A brief resumé of the structure and the processes involved in constructing knowledge-based expert systems is given. Reasons are given for the failure of these successful programs to be widely implemented. It is to be expected that improvements in other areas of artificial intelligence will make them more widely acceptable to the non-expert.

Artificial Intelligence↗

PubMed1986

Medical expert systems--knowledge tools for physicians.

Recent advances in the field of artificial intelligence have led to the emergence of expert systems, computational tools designed to capture and make available the knowledge of experts in a field. Although much of the underlying technology available today is derived from basic research on biomedical advice systems during the 1970s, medical application packages are thus far generally unavailable from the young artificial intelligence industry. Medical expert systems will begin to appear, however, as researchers in medical artificial intelligence continue to make progress in key areas such as knowledge acquisition, model-based reasoning and system integration for clinical environments. It is accordingly important for physicians to understand the current state of such research and the theoretic and logistic barriers that remain before useful systems can be made available. One experimental system, ONCOCIN, provides a glimpse of the kinds of knowledge-based tools that will someday be available to physicians.

Drug Therapy, Computer-Assisted↗

PubMed1994

International Commission for Protection Against Environmental Mutagens and Carcinogens. Two million rodent carcinogens? The role of SAR and QSAR in their detection.

The accurate prediction of chemical carcinogenicity can only be achieved by a balanced consideration of the following factors: the chemistry and metabolism of the test agent, the interaction between toxicity and genetic toxicity, the possibility of non-genotoxic events that trigger subsequent non-targeted mutagenesis, the difference between activities observed in vitro and in vivo, and the possible inadequacy and/or partiality of all datasets and observations. Extrapolation of activities within a series of congeners is usually possible, but predictions across different chemical classes/mechanisms of carcinogenicity are difficult. Artificial intelligence systems can be used to predict one or more of the above parameters given adequate learning sets, but the hope for a single, coherent and self-contained method of predicting all instances of carcinogenicity is unreal. The future of carcinogen/mutagen prediction lies with data-rich artificial intelligence systems based on known mechanistic principles used selectively within the context of chemical and biological human insight. The major current obstacle to progress is the assumption that mutagenicity and carcinogenicity are unitary phenomena that can be learned and predicted by artificial intelligence systems operating in isolation.

Alkylating Agents↗

PubMed2005

Bayesian networks: computer-assisted diagnosis support in radiology.

Medical knowledge is growing at an explosive rate. While the availability of pertinent data has the potential to make the task of diagnosis more accurate, it is also increasingly overwhelming for physicians to assimilate. Using artificial intelligence techniques, a computer can process large amounts of data to help physicians manage the growing body of medical knowledge and thereby make better decisions. Computer-assisted diagnosis support is of particular interest to the diagnostic imaging community because radiologists must integrate huge amounts of data in order to diagnose disease. Bayesian networks, among the most promising artificial intelligence techniques available, enable computers to store knowledge and estimate the probability of outcomes based on probability theory. The article describes what a Bayesian network is and how it works using a system in mammography for illustration. A comparison of Bayesian networks with other types of artificial intelligence methods, specifically neural networks and case-based reasoning, clarifies the unique features and the potential of these systems to aid radiologists in the decisions they make every day.

Bayes Theorem↗

PubMed2026

Proteomics in environmental pollution research: Advances, challenges, and future directions.

Environmental proteomics has emerged as a powerful approach for elucidating the molecular mechanisms underlying pollutant-induced biological effects. Although this field has developed rapidly, the systematic review of recent proteomics applications in environmental pollution research remains limited. This review explored the emerging roles of toxicoproteomics in biomarker discovery and mechanistic elucidation, as well as ecotoxicoproteomics in ecological risk assessment and bioremediation strategies. Here, we review the field, highlighting recent trends such as the integration of proteomics with genomics, transcriptomics, and metabolomics to provide a comprehensive view of biological responses to environmental stressors. We further discuss the growing application of artificial intelligence in improving proteomics data interpretation and accelerating biomarker discovery. In addition, recent technological advances in environmental proteomics are highlighted, including next-generation tissue microarray proteomics, nanoscale proteomics, single-cell proteomics, and spatial proteomics. Despite its potential, proteomics faces challenges, such as high operational costs, computational complexity in analysis, and technical limitations in low-abundance protein detection. We propose that the convergence of proteomics with artificial intelligence and multi-omics approaches offers promising solutions to these challenges, enhancing the practical application of proteomics in environmental monitoring and risk assessment.

Proteomics↗