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PubMed1990

Knowledge-based computer systems for radiotherapy planning.

Radiation therapy is one of the first areas of clinical medicine to utilize computers in support of routine clinical decision making. The role of the computer has evolved from simple dose calculations to elaborate interactive graphic three-dimensional simulations. These simulations can combine external irradiation from megavoltage photons, electrons, and particle beams with interstitial and intracavitary sources. With the flexibility and power of modern radiotherapy equipment and the ability of computer programs that simulate anything the machinery can do, we now face a challenge to utilize this capability to design more effective radiation treatments. How can we manage the increased complexity of sophisticated treatment planning? A promising approach will be to use artificial intelligence techniques to systematize our present knowledge about design of treatment plans, and to provide a framework for developing new treatment strategies. Far from replacing the physician, physicist, or dosimetrist, artificial intelligence-based software tools can assist the treatment planning team in producing more powerful and effective treatment plans. Research in progress using knowledge-based (AI) programming in treatment planning already has indicated the usefulness of such concepts as rule-based reasoning, hierarchical organization of knowledge, and reasoning from prototypes. Problems to be solved include how to handle continuously varying parameters and how to evaluate plans in order to direct improvements.

Artificial Intelligence↗
PubMedDate not supplied

Towards computer analysis of pulmonary infiltration.

A feasibility study is described to provide quantitative texture measures to distinguish between normal lung, alveolar infiltrates and interstitial infiltrates. Advanced computer imaging technology and decision making processes were applied to distinguish between these textural patterns. The results, based on computer extracted quantitative measures, show an excellent separation of the three classes considered with 95% accuracy in the training phase and 90% accuracy in the testing phase.

Diagnosis, Computer-Assisted↗
PubMed1988

Enhanced interpretation of diagnostic images.

In radiology, as in various other fields, observers study images to detect and diagnose underlying conditions. They make assessments of several image features and merge them into an overall decision. Demonstration is given here, in the context of mammography, that objective aids to this interpretative process can substantially improve accuracy, even for sophisticated and motivated radiologists. The aids are a checklist that solicits explicit, quantitative, systematic assessments of the important features of an image and a computer program that merges those assessments with optimal weights. The computer issues estimates of the likelihoods that specified conditions are present (in this study, the likelihood that a localized abnormality is malignant), and the radiologist benefits from taking those estimates as guidance.

Female↗
PubMed1988

A study of diagnostic accuracy in suspected acute appendicitis.

A prospective study was conducted of 276 patients with suspected acute appendicitis. Two hundred and eighteen patients had operations and 165 had acute appendicitis. Data from a previous retrospective study were used to develop an eight-item statistical program for a pocket calculator and this was made available to half the surgical registrars as an additional aid to clinical decision making. The other registrars involved in the study used only their clinical skills. There was no difference in diagnostic accuracy or frequency of operations or complications between patients managed by the two groups. Forty items of clinical data on each patient were collected prospectively. These were subsequently analysed on a computer program for the diagnosis of the acute abdomen which was developed in the United Kingdom. The accuracy of diagnosis of the clinicians was superior to the accuracy of the computer diagnosis.

Acute Disease↗
PubMed1990

The Bin Area Method: a computationally efficient technique for analysis of ventricular and atrial intracardiac electrograms.

Recent studies have reported a significant false positive rate in delivery of therapy by implantable antitachycardia devices utilizing detection algorithms based on sustained high rate. More selective decision schemes for the recognition of life-threatening arrhythmias have been recently proposed that use analysis of the intrinsic electrogram rather than rate alone. Morphological discrimination of abnormal electrograms using correlation waveform analysis (CWA) has been proposed as an effective method of intracardiac electrogram analysis, but its computational demands limit its use in implantable devices. A new method for intracardiac electrogram analysis, the bin area method (BAM), was created to detect abnormal cardiac conduction with computational requirements of one-half to one-tenth those of CWA. Like CWA, BAM is a template matching method that is sensitive to conduction changes revealed in the electrogram morphology and is independent of amplitude and baseline fluctuations. Performance of BAM and CWA were compared using bipolar right ventricular and right atrial electrode recordings from 47 patients undergoing clinical cardiac electrophysiology studies. Nineteen patients had 31 distinct monomorphic ventricular tachycardias (VTs) induced (group I), thirteen patients had paroxysmal bundle branch block of supraventricular origin (BBB) induced (group II), and 19 patients had retrograde atrial activation during right ventricular overdrive pacing (group III). (One patient was common to all three groups, and two patients were common to groups II and III.) Using the ventricular electrogram, both BAM and CWA distinguished VT from sinus rhythm in 28/31 (90%) cases, and BBB from Normal Sinus Rhythm (NSR) in 13/13 (100%) patients. Using the atrial electrogram, both BAM and CWA distinguished anterograde from retrograde atrial activation in 19/19 (100%) patients. BAM achieves similar performance to CWA with significantly reduced computational demands, and may make real-time analysis of intracardiac electrograms feasible for implantable pacemakers and antitachycardia devices.

Algorithms↗
PubMed1986

Computer aided diagnosis of acute abdominal pain: a multicentre study.

A multicentre study of computer aided diagnosis for patients with acute abdominal pain was performed in eight centres with over 250 participating doctors and 16,737 patients. Performance in diagnosis and decision making was compared over two periods: a test period (when a small computer system was provided to aid diagnosis) and a baseline period (before the system was installed). The two periods were well matched for type of case and rate of accrual. The system proved reliable and was used in 75.1% of possible cases. User reaction was broadly favourable. During the test period improvements were noted in diagnosis, decision making, and patient outcome. Initial diagnostic accuracy rose from 45.6% to 65.3%. The negative laparotomy rate fell by almost half, as did the perforation rate among patients with appendicitis (from 23.7% to 11.5%). The bad management error rate fell from 0.9% to 0.2%, and the observed mortality fell by 22.0%. The savings made were estimated as amounting to 278 laparotomies and 8,516 bed nights during the trial period--equivalent throughout the National Health Service to annual savings in resources worth over 20m pounds and direct cost savings of over 5m pounds. Computer aided diagnosis is a useful system for improving diagnosis and encouraging better clinical practice.

Abdomen↗
PubMed1979

An approach to quality and performance control in a computer-assisted clinical chemistry laboratory.

A locally developed, computer-based clinical chemistry laboratory system has been in operation since 1970. This utilises a Digital Equipment Co Ltd PDP 12 and an interconnected PDP 8/F computer. Details are presented of the performance and quality control techniques incorporated into the system. Laboratory performance is assessed through analysis of results from fixed-level control sera as well as from cumulative sum methods. At a simple level the presentation may be considered purely indicative, while at a more sophisticated level statistical concepts have been introduced to aid the laboratory controller in decision-making processes.

Chemistry, Clinical↗
PubMed1975

On the evolution of the physiological model.

Most of us who have concerned ourselves with models can perceive outlines like those above to catalog the future evolution of the expository function of models. In the context of a single class of computerized mathematical models of respiratory physiology, we can observe at once the burgeoning interest among scientists, and the similarities between model activity and the general organization of scientific information for use. Although physiological models have become quite advanced in their subject control, there is relatively little coordinated activity in the mechanization of the purposes and philosophical potential of automata. The outlines, however, are visible. An assiduous pursuit of the notion of "explanation" by machine is a major evolutionary step next to occur. It appears to us that various diagrams similar to Figures 5 or 6 can be created and investigated in terms of their relation to the human mind and in terms of formalizing rules for traversing from one plane to the next. The evolution of models will require program-making programs which can decide when and how to aggregate for deductive inference, and how far to penetrate top-down for explanation. The rules for identifying "second order" effects must be established. The decision to ignore or use these rules will be crucial. These are the means whereby the systems are traversed from plane to plane. In a word, models need to synthesize the means to ignore, "forget," and gloss over; only then will we have useful tools for taking informed action in physiology, diagnosis in medicine, or the writing of "scholarly" reviews.

Computers↗
PubMedDate not supplied

Feasibility of physician-developed expert systems.

The authors developed an experimental domain-independent "expert system generator" intended for direct use by physicians. They then undertook a four-year study to determine whether physicians could use such a system effectively. During this period they taught the use of the expert system generator to 70 medical students, who utilized it to build two small medical expert systems. At the conclusion of the course, students were examined on decision-making concepts and completed anonymous questionnaires. Performance scores, a composite of test and project grades, were calculated for each student. There was no significant association between previous computer experience and performance score. Thirty-two of 47 students responding felt the expert system generator was easy to use; 15 felt it was of moderate difficulty. Forty-three of 47 thought it a useful teaching aid. These data support the conclusion that physicians can learn to use domain-independent software to implement medical expert systems directly, without a knowledge engineer as an intermediary.

Artificial Intelligence↗
PubMedDate not supplied

Sequential decision making with continuous disease states and measurements: II. Application to diastolic blood pressure.

The model and strategy for sequential decision making using normally distributed measurements proposed in a companion paper are applied to the problem of diagnosing diastolic hypertension. The assumptions of the model are discussed and justified clinically. Methods for assigning values to the model's parameters are explained and illustrated in the context of a hypothetical "generic" patient. Although current national recommendations and the sequential strategy both lead to an average of 1.89 measurements per patient prior to diagnosis, the sequential strategy applies a sequence of four or more measurements to 12% of patients. Fewer than 1% of patients would require ten or more measurements under this strategy. The sequential strategy leads to fewer patients' receiving unnecessary treatment and substantially higher expected utility for the patient. The role of multiple blood pressure determinations per visit is explored in the absence of appropriate estimates. Even under "best-case" assumptions, however, it is shown that obtaining more than one observation per visit is called for only in about 15% of visits. While the exact role of multiple determinations cannot be specified from existing data, it is likely to be much more limited than current recommendations suggest.

Computer Simulation↗
PubMed1991

An objective, noninvasive method for the diagnosis of temporomandibular joint disorders.

An objective, noninvasive method for the diagnosis and monitoring of pathology referable to the temporomandibular joint is presented. A computerized temporomandibular joint analyzer was developed to obtain data from the patient's temporomandibular joint area to provide an objective record of clinical symptoms and signs. This data, used in conjunction with a complete history and physical examination, aids in the diagnosis and decision-making process for therapy. Fifty patients with temporomandibular complaints were evaluated. Diagnoses made using the temporomandibular joint analyzer in conjunction with complete histories and physical examinations were confirmed in 81% of the patients by further examinations with x-ray studies, computed tomography scans, magnetic resonance imaging, and arthroscopy. Patients' progress during treatment was monitored using the temporomandibular joint analyzer. Results of the temporomandibular joint analyzer were compatible with patient's progress as reported subjectively and with clinical evaluation in 97% of the cases. The temporomandibular joint analyzer can be used to reduce time and expense as well as to minimize the discomfort involved in the evaluation of temporomandibular joint disorders.

Adolescent↗
PubMed1991

A computer-controlled ventilator weaning system.

Weaning of patients from mechanical ventilation is a time-consuming, labor-intensive process. Because most weaning decisions are based on objective data, we tested a computer-directed weaning system on postoperative patients. We developed an automatic, computer-controlled ventilator weaning system which interfaces a laptop computer to a ventilator and a pulse oximeter. The laptop computer program accesses patient data through the ventilator and pulse oximeter to make weaning decisions. The computer directly controls the ventilator through an interface developed for this system. We tested the system in nine postoperative patients who met the following criteria: negative inspiratory force less than or equal to -20 cm H2O, vital capacity greater than 10 ml/kg, inspired oxygen concentration less than or equal to 40 percent, and satisfactory arterial blood gas parameters (pH between 7.32 and 7.48, PCO2 between 32 and 48, and oxygen saturation greater than or equal to 90 percent). The computer decreased the SIMV rate by 2 breaths/min every 5 min until a rate of 2 breaths/min was reached, then decreased pressure support by 4 cm H2O every 5 min as long as the patient met the following criteria: respiratory rate between 8 and 25 breaths/min, minute ventilation between 6 and 14 L, and pulse oximeter oxygen saturation greater than or equal to 90 percent. If unsatisfactory weaning criteria were noted, the system automatically returned the patient to the previous weaning level. We successfully weaned nine patients using the system. Additional studies are underway to determine if this system can be used in medical patients. We believe this computer-controlled ventilator weaning system can be used successfully in patients requiring mechanical ventilation and may decrease the time and cost associated with the care of these patients.

Adult↗
PubMed2026

Automated Classification of Lymphoma Subtypes From Histopathological Images Using a U-Net Deep Learning Model: Comparative Evaluation Study.

BACKGROUND: Accurate classification and grading of lymphoma subtypes are essential for treatment planning. Traditional diagnostic methods face challenges of subjectivity and inefficiency, highlighting the need for automated solutions based on deep learning techniques. OBJECTIVE: This study aimed to investigate the application of deep learning technology, specifically the U-Net model, in classifying and grading lymphoma subtypes to enhance diagnostic precision and efficiency. METHODS: In this study, the U-Net model was used as the primary tool for image segmentation integrated with attention mechanisms and residual networks for feature extraction and classification. A total of 620 high-quality histopathological images representing 3 major lymphoma subtypes were collected from The Cancer Genome Atlas and the Cancer Imaging Archive. All images underwent standardized preprocessing, including Gaussian filtering for noise reduction, histogram equalization, and normalization. Data augmentation techniques such as rotation, flipping, and scaling were applied to improve the model's generalization capability. The dataset was divided into training (70%), validation (15%), and test (15%) subsets. Five-fold cross-validation was used to assess model robustness. Performance was benchmarked against mainstream convolutional neural network architectures, including fully convolutional network, SegNet, and DeepLabv3+. RESULTS: The U-Net model achieved high segmentation accuracy, effectively delineating lesion regions and improving the quality of input for classification and grading. The incorporation of attention mechanisms further improved the model's ability to extract key features, whereas the residual structure of the residual network enhanced classification accuracy for complex images. In the test set (N=1250), the proposed fusion model achieved an accuracy of 92% (1150/1250), a sensitivity of 91.04% (1138/1250), a specificity of 89.04% (1113/1250), and an F1-score of 90% (1125/1250) for the classification of the 3 lymphoma subtypes, with an area under the receiver operating characteristic curve of 0.95 (95% CI 0.93-0.97). The high sensitivity and specificity of the model indicate strong clinical applicability, particularly as an assistive diagnostic tool. CONCLUSIONS: Deep learning techniques based on the U-Net architecture offer considerable advantages in the automated classification and grading of lymphoma subtypes. The proposed model significantly improved diagnostic accuracy and accelerated pathological evaluation, providing efficient and precise support for clinical decision-making. Future work may focus on enhancing model robustness through integration with advanced algorithms and validating performance across multicenter clinical datasets. The model also holds promise for deployment in digital pathology platforms and artificial intelligence-assisted diagnostic workflows, improving screening efficiency and promoting consistency in pathological classification.

Humans↗
PubMedDate not supplied

Characteristics of the software for computer applications in medicine.

The requirements of clinical medicine which have tended to make the design and implementation of software for hospital computer systems more difficult than that elsewhere, are discussed in this paper. Specific constraints on the software for selected computer-assisted activities in a hospital environment are examined in considerable depth. It is shown that since some of these activities have counterparts elsewhere, hospital computing can benefit from the accumulated experience in dealing with similiar problems in business and scientific environments. The argument is put forward that developing countries, with their characteristic problem of acute shortage of skilled manpower in both medicine and computing, should initially concentrate on applying computers to these activities alone. Furthermore, medical education in such countries should incorporate programmes relating to computer technology in general and the software aspects in particular.

Computers↗
PubMed1989

Addressing challenges in nursing informatics instruction.

Objectives for the interactive experiences include exposure to a wide variety of computer applications; in-depth experience with at least one application; overcoming computer fear; appreciating the rigid, logical flow of computerized problem-solving; and appreciating the benefits and limitations of computer applications for a variety of purposes. Ultimately, a major purpose of transferring informatics content is related to stimulating students' imagination with respect to the computer's ability to aid their professional endeavors. Robinson (1984) describes the imagination factor: "I think the total potential of computers is only limited by our imagination. It's like giving an artist a palette that has an infinite number of colors, some of them invisible to the naked eye. . . It is a tool for the realization of ideas." Students who have been exposed to the benefits of major categories of computer applications can appreciate computer capabilities with respect to exploring scientific and nursing phenomena, and building databases to store and access information (Newbern, 1985). These students should have enough theoretical and experimental knowledge of computers to become actively involved in making creative, informed decisions about how computers will be applied to nursing in their professional setting. Hardin and Skiba (1982) noted a gap between the powerful information processing capabilities of the computer and its relatively limited use by nursing--a gap which still exists today. Students who have participated in an idea generation course on computer applications can help to bridge this gap, helping nursing to take full advantage of the computer-saturated environments of the future.

Computer User Training↗
PubMed1988

Introduction of a computer-based oncology patient-care system in a teaching hospital.

This report describes a computer-based patient-care system for oncology that provides physicians, nurses, medical students and associated health-care personnel with the means to retrieve data and reports that will enhance greatly their capacity to make informed decisions regarding patient management; at the same time such a system provides adequate safeguards for the confidentiality of information. The system provides information from all clinical laboratories, nursing staff observations, and pharmacy, radiology, pathology and outpatients departments. In addition, the system generates treatment plans that relate directly to day-to-day patient management, covering all aspects of patient care. More extensive research data, demographic data and reports are available from the computer's large database. This computer system is called the Oncology Research Centre Information System (ORCIS). The development of ORCIS has been a major initiative of the NSW State Cancer Council, which is encouraging teaching hospitals in New South Wales to consider this system. The aim of this initiative is to try to prevent the development of many different computer systems in New South Wales, which would cause further confusion in the management of medical information.

Costs and Cost Analysis↗