Computer-assisted Medical Decision Research Articles

Automated Computer-Assisted Medical Decision-Making System Based on Morphological Shape and Skin Thickness Analysis for Asymmetry Detection in Mammographic Images.

Breast cancer is a significant health concern for women, emphasizing the need for early detection. This research focuses on developing a computer system for asymmetry detection in mammographic images, employing two critical approaches: Dynamic Time Warping (DTW) for shape analysis and the Growing Seed Region (GSR) method for breast skin segmentation. The methodology involves processing mammograms in DICOM format. In the morphological study, a centroid-based mask is computed using extracted images from DICOM files. Distances between the centroid and the breast perimeter are then calculated to assess similarity through Dynamic Time Warping analysis. For skin thickness asymmetry identification, a seed is initially set on skin pixels and expanded based on intensity and depth similarities. The DTW analysis achieves an accuracy of 83%, correctly identifying 23 possible asymmetry cases out of 20 ground truth cases. The GRS method is validated using Average Symmetric Surface Distance and Relative Volumetric metrics, yielding similarities of 90.47% and 66.66%, respectively, for asymmetry cases compared to 182 ground truth segmented images, successfully identifying 35 patients with potential skin asymmetry. Additionally, a Graphical User Interface is designed to facilitate the insertion of DICOM files and provide visual representations of asymmetrical findings for validation and accessibility by physicians.

Artificial intelligence and pediatrics: A synthetic mini review

The use of artificial intelligence intelligencein medicine can be traced back to 1968 when Paycha published his paper Le diagnostic a l'aide d'intelligences artificielle, presentation de la premiere machine diagnostri. Few years later Shortliffe et al. presented an expert system named Mycin which was able to identify bacteria causing severe blood infections and to recommend antibiotics. Despite the fact that Mycin outperformed members of the Stanford medical school in the reliability of diagnosis it was never used in practice due to a legal issue who do you sue if it gives a wrong diagnosis?. However only in 2016 when the artificial intelligence software built into the IBM Watson AI platform correctly diagnosed and proposed an effective treatment for a 60-year-old womans rare form of leukemia the AI use in medicine become really popular.On of first papers presenting the use of AI in paediatrics was published in 1984. The paper introduced a computer-assisted medical decision making system called SHELP.

Computer-assisted Medical Decision-making System for Diagnosis of Urticaria.

Background: Urticaria is a common allergic disease that affects all age groups. Allergic disorders are diagnosed at allergy testing centers using skin tests. Though skin tests are the gold standard tests for allergy diagnosis, specialists are required to interpret the observations and test results. Hence, a computer-assisted medical decision-making (CMD) system can be used as an aid for decision support, by junior clinicians, in order to diagnose the presence of urticaria. Methods: The data from intradermal skin test results of 778 patients, who exhibited allergic symptoms, are considered for this study. Based on food habits and the history of a patient, 40 relevant allergens are tested. Allergen extracts are used for skin test. Ten independent runs of 10-fold cross-validation are used to train the system. The performance of the CMD system is evaluated using a set of test samples. The test samples were also presented to the junior clinicians at the allergy testing center to diagnose the presence or absence of urticaria. Results: From a set of 91 features, a subset of 41 relevant features is chosen based on the relevance score of the feature selection algorithm. The Bayes classification approach achieves a classification accuracy of 96.92% over the test samples. The junior clinicians were able to classify the test samples with an average accuracy of 75.68%. Conclusion: A probabilistic classification approach is used for identifying the presence or absence of urticaria based on intradermal skin test results. In the absence of an allergy specialist, the CDM system assists junior clinicians in clinical decision making.

Co-Design of a Computer-Assisted Medical Decision Support System to Manage Antibiotic Prescription in an ICU Ward.

About 37 thousand people die per year in Europe due to infections by resistant bacteria. Fighting antimicrobial resistances (AR) is a top priority to save lives and reduce costs. AR is triggered mostly by uncritical antibiotic prescription. This paper presents HAITool, a decision-making information system to support antibiotic prescription. The system was co-developed together with health professionals using Design Science Research Methodology, empowered with innovative data visualization techniques to improve AR management. HAITool includes integrated visualizations of patient, microbiology, and pharmacy data, facilitating clinical decision support, antibiotic prescriptions quality and antibiotic-resistant bacteria monitoring. It also includes an alert module that monitors conformance of antibiotic prescriptions with norms and guidelines. HAITool is evaluated using both the Österle principles and interviews with physicians and infection control team from three participant hospitals.

Medical plan-analysis by computer

The traditional approach to computer-assisted medical decision-making involves designing a computer system which simulates a physician's decision-making process. This paper describes a different approach: Medical Plan-Analysis (MPA). Instead of trying to tell a physician how best to manage a patient, an MPA system critiques a physician's management plan. It is anticipated that this approach may have a number of potential social, medical, and medicolegal advantages. The MPA approach has been implemented in ATTENDING, a system designed using Artificial Intelligence techniques to critique a physician's plan for anesthetic management.

Medical Plan-Analysis by computer

The traditional approach to computer-assisted medical decision-making involves designing a computer system which simulates a physician's decision-making process. This paper describes a different approach: Medical Plan-Analysis (MPA). Instead of trying to tell a physician how best to manage a patient, an MPA system critiques a physician's management plan. It is anticipated that this approach may have a number of potential social, medical, and medicolegal advantages. The MPA approach has been implemented in ATTENDING, a system designed using Artificial Intelligence techniques to critique a physician's plan for anesthetic management.

From the editor

This issue we break from tradition. We have no guest editor for September buf present a potpourri of papers and staff articles. Due to space limitations we were unable to publish Dr. Reggia's fine paper on computer-assisted medical decision making in the June issue. We also want to apprise our readers of computer use in both dentistry and veterinary medicine - areas where computers are also having a major impact. Importantly, we feature another look at education in biomedical engineering. White and Plonsey's paper should put to rest comments about the inadequate education of bioengineers. Our article on internships for clinical engineers features the bright and not so bright sides of current programs. Our other staff paper on bioengineering at the University of Utah swings positive to show what a dedicated group of faculty can do to make their institution a truly outstanding one. In addition, Webster gives us two reports on bioengineering education and research developments in two other countries China and Brazil. We also reprint an article about a fascinating bit of bioengineering history and entrepreneurship - the development of a commercial pacemaker and the founding of Medtronic, Inc. Patent Editor Ron Cohn was kind enough to submit a paper on the patent and copyright aspects of computer programs. This is particularly appropriate since the issue of protection of programs is far from resolved.

Computer-assisted medical decision-making: interest growing

Computer-assisted medical decision-making: interest growing

Computer-assisted medical decision-making: interest growing

"C omputers can never replace physicians. Only physicians have the ability to reason logically and to mix that reason with intuition, experience, and ethics—something no machine can ever learn to do." Or so many physicians have been telling people proposing computer-assisted medical decision making (CMD) since the early 1960s, and controversy has continued. But now it looks like the two sides are nearing agreement: Computers may never replace physicians, but they can and will assist their decisions. "Assist" is the key word in CMD. Early advocates tended to overestimate the powers of the computer and promised complete clinical evaluation at the push of a button. It was natural for physicians to feel somewhat threatened. It was also natural that, with experience, such promises had to be modified. Today there still are only several functioning knowledge-based decision systems—called "knowledge-based" because they make inferences using a data base of medical information stored

Computer-assisted medical decision making: a critical review.

This paper presents an overview of the concepts underlying computer-assisted medical decision-making (CMD) systems. Alternative approaches to constructing CMD systems are reviewed, including “conventional programming techniques,” statistical pattern classification, rule-based deduction, modelling of diagnostic reasoning, and data base comparisons. Each of these methods is illustrated with examples taken from pulmonary medicine. Although much progress has been made over the last two decades, all existing methods for creating CMD systems are seen to be limited in their ability to represent and process medical knowledge. Other problems facing the field include the time and cost of CMD system development, physician resistance to using these systems, the question of transferability, and the lack of large, reliable data bases for many clinical problems. Recent trends that may overcome these problems are identified and discussed.

A model-based method for computer-aided medical decision-making

A general method of computer-assisted medical decision-making has been developed based on causal-associational network (CASNET) models of disease. A CASNET model consists of three main components: observations of a patient, pathophysiological states, and disease classifications. As observations are recorded, they are associated with the appropriate states. States are causally related, forming a network that summarizes the mechanisms of disease. Patterns of states in the network are linked to individual disease classifications. Recommendations for broad classes of treatment are triggered by the appropriate diagnostic classes. Strategies of specific treatment selection are guided by the individual pattern of observations and diagnostic conclusions. This approach has been applied in a consultation program for the diagnosis and treatment of the glaucomas.

Computer-Aided Medical Decision Making in Radiotherapy

Radiotherapy departments are becoming sophisticated in working with computers for isodose computations, treatment machine verifications and administrative and medical records. The next step lies in computer-assisted medical decision making. The logic for a patient's diagnostic work-up and treatment protocol can be stored in a computer. It can then be used as an aid in making the diagnosis, in prescribing the treatment and for quality control. For patients who fit established protocols the computer can select and list treatment using the logic of that protocol. Such a system has been implemented for the postoperative radiotherapy of breast cancer on a trial basis. Its potential usefulness is illustrated by results in 25 consecutive patients. Physician acceptance and costs of the program are under investigation.