Stanley Desmond Smith. 3 March 1931 — 1 August 2023

The preferred embodiments described herein provide a medical diagnostic ultrasound imaging system with a patient support surface. In one preferred embodiment, a medical diagnostic ultrasound imaging system is provided that is integrated with a base coupled with a patient support surface. In another preferred embodiment, a medical diagnostic ultrasound imaging system is carried by a medical diagnostic ultrasound imaging system cart that is detachably coupled with a base coupled with a patient support surface. In yet another preferred embodiment, a first medical diagnostic ultrasound imaging system assembly is provided that is integrated with a base coupled with a patient support surface. A medical diagnostic ultrasound imaging system cart is also provided that carries a second medical diagnostic ultrasound imaging system assembly and is detachably and electrically coupled with the base. Other preferred embodiments are provided, and each of these preferred embodiments can be used alone or in combination with one another.

The object of research is the diagnostic decision support system (DSS). One of the most problematic areas in medical diagnostic systems is the formation of a knowledge base based on expert rules, which provides a recommendation for the disease. The methods of designing medical diagnostic systems have been studied. Methods for applying the potential of artificial intelligence in medicine in the form of fuzzy rules or conducting diagnostics on the basis of Bayesian networks are considered. Intellectual computing tools in the form of expert systems based on rules and fuzzy logic, applied to neural networks and genetic algorithms performed in medical diagnostics are considered.To develop a decision support system for a pediatrician, a method to build a knowledge base on the basis of logical rules «If ..., then ...» was chosen. Using this method allows to create initial conditions for input data in the system, and speed up their processing in the knowledge base. Although the knowledge base is quite cumbersome, this does not reduce the performance of the system.In the process of research, the development of a medical diagnostic system for decision support by a pediatrician for the design stages is described. The application of this system allows to automate the process of document circulation for a pediatrician and to speed up the stage of preliminary assessment of the patient's condition.The built-in pediatrician electronic pediatric module not only automates the workflow process, reduces the doctor's work time with papers, but also allows to obtain complete information about the patient.The calculation of economic efficiency from the DSS introduction by a pediatrician is performed. The system cost is to be recouped within 1 year.The prospect of adding modules to the system for individual diseases and forming an electronic record from the moment of birth with the prospect of transferring data to the system for adults are advantages over analogues of this software product.

Considerable research is being conducted in the area of expert systems for diagnosis. Early work was concentrated in medical diagnostic systems (Clancey and Shortliffe, {21}). MYCIN (Buchanan and Shortliffe, {15}) appears to represent the first medical diagnostic expert system. Current efforts are expanding to the area of equipment maintenance and diagnostics, with numerous systems having been built during the past several years. We concentrate our effort in this survey on expert systems for diagnosis and refer the reader to (Hayes-Roth, Waterman, and Lenat, {42} Hayes-Roth, {43}), (Waterman, {101}), and (Charniak and McDermott, {19}) for introductions to expert systems. We remark that it is common for diagnostic systems to integrate concepts from artificial intelligence (expert systems), decision theory, and operations research.

The development of medical diagnostic system has been one of the main research fields during years. The goal of the medical diagnostic system is to place a nosological system that could ease the diagnostic evaluation normally performed by scientists and doctors. Efficient diagnostic evaluation is essentials and requires broad knowledge in order to improve conventional diagnostic system. Several approaches on developing the medical diagnostic system have been designed and tested since the earliest 60s. Attempts on improving their performance have been made which utilizes the fields of artificial intelligence, statistical analyses, mathematical model and engineering theories. With the availability of the microcomputer and software development as well as the promising aforementioned fields, medical diagnostic prototypes could be developed. In general, the medical diagnostic system consists of several stages, namely the 1) data acquisition, 2) feature extraction, 3) feature selection, and 4) classifications stages. Data acquisition stage plays an important role in converting the inputs measured from the real world physical conditions to the digital numeric values that can be manipulated by the computer system. One of the common medical inputs could be medical microscopic images, radiographic images, magnetic resonance image (MRI) as well as medical signals such as electrocardiogram (ECG) and electroencephalogram (EEG). Normally, the scientist or doctors have to deal with myriad of data and redundant to be processed. In order to reduce the complexity of the diagnosis process, only the significant features of the raw data such as peak value of the ECG signal or size of lesion in the mammogram images will be extracted and considered in the subsequent stages. Mathematical models and statistical analyses will be performed to select the most significant features to be classified. The statistical analyses such as principal component analysis and discriminant analysis as well as mathematical model of clustering technique have been widely used in developing the medical diagnostic systems. The selected features will be classified using mathematical models that embedded engineering theory such as artificial intelligence, support vector machine, neural network and fuzzy-neuro system. These classifiers will provide the diagnostic results without human intervention. Among many publishable researches, several prototypes have been developed namely NeuralPap, Neural Mammo, and Cervix Kit. The former system (NeuralPap) is an automatic intelligent diagnostic system for classifying and distinguishing between the normal and cervical cancerous cells. Meanwhile, the Cervix Kit is a portable Field-programmable gate array (FPGA)-based cervical diagnostic kit that could automatically diagnose the cancerous cell based on the images obtained during sampling test. Besides the cervical diagnostic system, the Neural Mammo system is developed to specifically aid the diagnosis of breast cancer using a fine needle aspiration image.

In the era of data technologies for medical diagnostic cognitive software systems, new informative data has been obtained based on topological data analysis in the form of Betti numbers. These new, more informative data can be applied to medical diagnostic cognitive software systems and obtain a higher accuracy in the diagnosis of neurodegenerative diseases, which is extremely important, since the choice of a patient's treatment protocol depends on their accuracy. The higher accuracy of the functioning of medical diagnostic cognitive software systems is achieved due to the fact that new informative data are topological data, which in their values take into account the nature of the topological structure of experimentally measured data in the form of electroencephalographic (EEG) signals characterizing the activity of the patient's brain. On the basis of experimental data - EEG signals and methods of data science - topological data analysis, new more informative topological data were obtained for the development of high-precision medical diagnostic cognitive software systems in neurology. The scientific approach is based on the methods and analytical techniques of algebraic topology, in particular, the theory of categories and simplicial geometry (simplicial complexes). In particular, topological data – Betti numbers, obtained on the basis of topological analysis of data on experimentally measured EEG signals of the human brain, represent the number of simplexes with holes of different dimensions of the Vietoris-Rips simplex complex.

Framework for eliciting knowledge for a medical laboratory diagnostic expert system

With the progress of science and technology, material life is getting better and better now, so people have begun to have higher and higher requirements for life and longevity has more and more yearning. In ancient times are through artificial diagnosis to find out the physical condition, Chinese medicine is expected to smell cut the four major methods of diagnosis and treatment, but can achieve this skill only a small number of people, and most of the disease can not be identified, not good to treat patients. So now with the progress of science and technology, technology and intelligent rapid development, artificial intelligence may be able to make some contributions to the diagnosis of the disease. Therefore, the purpose of this paper is to design artificial intelligence-based medical diagnostic system to update. In this paper, after identifying the basic structure of artificial intelligence and constructing the database, we understand the diagnosis methods of medical diagnostic system and other diagnostic systems, and finally, the medical diagnostic system can be updated by using the phase-changing algorithm, so that it can better fit with artificial intelligence, so as to ensure the success rate of treatment and the correct rate of diagnosis. Experimental results show that the use of artificial intelligence as a basis for medical diagnostic systems can better identify the disease and make complementary treatment options.

AI systems are increasingly being developed to provide the first point of contact for patients. These systems are typically focused on question-answering and integrating chat systems with diagnostic algorithms, but are likely to suffer from many of the same deficiencies in explanation that have plagued medical diagnostic systems since the 1970s ( Shortliffe, 1979 ). To provide better guidance about how such systems should approach explanations, we report on an interview study in which we identified explanations that physicians used in the context of re-diagnosis or a change in diagnosis. Seven current and former physicians with a variety of specialties and experience were recruited to take part in the interviews. Several high-level observations were made by reviewing the interview notes. Nine broad categories of explanation emerged from the thematic analysis of the explanation contents. We also present these in a diagnosis meta-timeline that encapsulates many of the commonalities we saw across diagnoses during the interviews. Based on the results, we provided some design recommendations to consider for developing diagnostic AI systems. Altogether, this study suggests explanation strategies, approaches, and methods that might be used by medical diagnostic AI systems to improve user trust and satisfaction with these systems.

Articles about medical diagnostic decision support (MDDS) systems often begin with a disclaimer such as, "despite many years of research and millions of dollars of expenditures on medical diagnostic systems, none is in widespread use at the present time." While this statement remains true in the sense that no single diagnostic system is in widespread use, it is misleading with regard to the state of the art of these systems. Diagnostic systems, many simple and some complex, are now ubiquitous, and research on MDDS systems is growing. The nature of MDDS systems has diversified over time. The prospects for adoption of large-scale diagnostic systems are better now than ever before, due to enthusiasm for implementation of the electronic medical record in academic, commercial, and primary care settings. Diagnostic decision support systems have become an established component of medical technology. This paper provides a review and a threaded bibliography for some of the important work on MDDS systems over the years from 1954 to 1993.

In order to improve the accuracy of diagnosis and the effectiveness of treatment on chronic diseases, a parallel medical diagnostic and treatment system for chronic diseases is proposed in this paper. The system consists of the artificial system, computational experiments and parallel execution. Generally, the actual medical system includes real doctors, real patients, and the process of real doctors diagnosing and creating a treatment plan. Based on the components in actual medical system, the virtual doctors and virtual patients are created in artificial system. The computational experiments are to exhaustively explore and evaluate reasonable diagnoses and treatment options, so that the most accurate diagnosis and the best treatment plan are configured. The parallel execution between the artificial and the actual system includes the interaction between virtual doctors and real doctors as well as that between the virtual patients and actual patients. The parallel execution, on the doctors' end, is to ensure the precise diagnosis of diseases and the effectiveness of the treatment plan. On the other hand, the parallel execution, on the patients' end, is to provide patients with more personal guidance on following the treatment and to assist sending feedback to the system regarding the treatment effectiveness. As the chronic diseases generally require a long-term treatment, this parallel system can better accommodate the process by continuously monitoring the treatment and sending feedback to the model so as to actively optimize the treatment plan.

Imaging of tissue elastic properties is a relatively new and powerful approach to one of the oldest and most important diagnostic tools. Imaging of shear wave speed with ultrasound is has been added to most high-end ultrasound systems. Understanding this exciting imaging mode aiding its most effective use in medicine can be a rewarding effort for medical physicists and other medical imaging and treatment professionals. Assuring consistent, quantitative measurements across the many ultrasound systems in a typical imaging department will constitute a major step toward realizing the great potential of this technique and other quantitative imaging. This session will target these two goals with two presentations. A. Basics and Current Implementations of Ultrasound Imaging of Shear Wave Speed and Elasticity - Shigao Chen, Ph.D. Learning objectives-To understand: 1. Introduction: • Importance of tissue elasticity measurement • Strain vs. shear wave elastography (SWE), beneficial features of SWE • The link between shear wave speed and material properties, influence of viscosity 2. Generation of shear waves • External vibration (Fibroscan) • ultrasound radiation force • Point push • Supersonic push (Aixplorer) • Comb push (GE Logiq E9) 3. Detection of shear waves • Motion detection from pulse-echo ultrasound • Importance of frame rate for shear wave imaging • Plane wave imaging detection • How to achieve high effective frame rate using line-by-line scanners 4. Shear wave speed calculation • Time to peak • Random sample consensus (RANSAC) • Cross correlation 5. Sources of bias and variation in SWE • Tissue viscosity • Transducer compression or internal pressure of organ • Reflection of shear waves at boundaries B. Elasticity Imaging System Biomarker Qualification and User Testing of Systems – Brian Garra, M.D. Learning objectives-To understand: 1. Goals • Review the need for quantitative medical imaging • Provide examples of quantitative imaging biomarkers • Acquaint the participant with the purpose of the RSNA Quantitative Imaging Biomarker Alliance and the need for such an organization • Review the QIBA process for creating a quantitative biomarker • Summarize steps needed to verify adherence of site, operators, and imaging systems to a QIBA profile 2. Underlying Premise and Assumptions • Objective, quantifiable results are needed to enhance the value of diagnostic imaging in clinical practice • Reasons for quantification i. Evidence based medicine requires objective, not subjective observer data ii. Computerized decision support tools (eg CAD) generally require quantitative input. iii. Quantitative, reproducible measures are more easily used to develop personalized molecular medical diagnostic and treatment systems 3. What is quantitative imaging? • Definition from Imaging Metrology Workshop 4. The Quantitative Imaging Biomarker Alliance • Formation 2008 • Mission • Structure • Example Imaging Biomarkers Being Explored • Biomarker Selection • Groundwork • Draft Protocol for imaging and data evaluation • QIBA Profile Drafting • Equipment and Site Validation i. Technical ii. Clinical • Site and Equipment QA and Compliance Checking 5. Ultrasound Elasticity Estimation Biomarker • US Elasticity Estimation Background • Current Status and Problems • Biomarker Selection-process and outcome 6. US SWS for Liver Fibrosis Biomarker Work • Groundwork i. Literature search and analysis results ii. Phase I phantom testing-Elastic phantoms iii. Phase II phantom testing-Viscoelastic phantoms iv. Digital Simulated Data • Protocol and Profile Drafting i Protocol: based on UPICT and existing literature and standards bodies protocols ii. Profile-Current claims, Manufacturer specific appendices 7. What comes after the profile • Profile Validation i Technical validation ii. Clinical validation • QA and Compliance i. Possible approaches 1. Site a. Operator testing b. Site protocol re-evaluation 2. Imaging system a. Manufacturer testing and attestation b. User acceptance testing and periodic QA i. Phantom Tests ii. Digital Phantom Based Testing iii. Standard QA Testing iv. Remediation Schemes 8. Profile Evolution • Towards additional applications • Towards higher accuracy and precision Supported in part by NIH contract HHSN268201300071C from NIBIB. Collaboration with GE Global Research, no personal support.; S. Chen, Some technologies described in this presentation have been licensed. Mayo Clinic and Dr. Chen have financial interests these technologies.

The preferred embodiments described herein provide a medical diagnostic ultrasound imaging system and method in which a non-real-time operating system is used to transfer image data. In one preferred embodiment, a medical diagnostic ultrasound imaging system includes a central processing unit (“CPU”) located outside of the received signal path to transfer image data. The non-real-time operating system can be used, for example, to transfer image data from a hard disk to system memory for display, to transfer image data from system memory to the hard disk for storage, or to scroll image data to view a loop of image data.

The preferred embodiments described herein provide medical diagnostic ultrasound imaging system transmitter control in a modular transducer system. With these preferred embodiments, transmitters in a medical diagnostic ultrasound imaging system are enabled only when contacts in a scan head are electrically coupled with contacts in a receptacle assembly of a modular transducer system. This prevents high voltages from developing in the receptacle assembly when the scan head is removed from or is not fully engaged with the receptacle assembly. In one preferred embodiment, a detector is used to detect movement of a member comprising the contacts in the receptacle assembly. Other preferred embodiments are provided, and each of the preferred embodiments described herein can be used alone or in combination with one another.

BackgroundRecently, machine learning (ML) has been transforming our daily lives by enabling intelligent voice assistants, personalized support for purchase decisions, and efficient credit card fraud detection. In addition to its everyday applications, ML holds the potential to improve medicine as well, especially with regard to diagnostics in clinics. In a world characterized by population growth, demographic change, and the global COVID-19 pandemic, ML systems offer the opportunity to make diagnostics more effective and efficient, leading to a high interest of clinics in such systems. However, despite the high potential of ML, only a few ML systems have been deployed in clinics yet, as their adoption process differs significantly from the integration of prior health information technologies given the specific characteristics of ML.ObjectiveThis study aims to explore the factors that influence the adoption process of ML systems for medical diagnostics in clinics to foster the adoption of these systems in clinics. Furthermore, this study provides insight into how these factors can be used to determine the ML maturity score of clinics, which can be applied by practitioners to measure the clinic status quo in the adoption process of ML systems.MethodsTo gain more insight into the adoption process of ML systems for medical diagnostics in clinics, we conducted a qualitative study by interviewing 22 selected medical experts from clinics and their suppliers with profound knowledge in the field of ML. We used a semistructured interview guideline, asked open-ended questions, and transcribed the interviews verbatim. To analyze the transcripts, we first used a content analysis approach based on the health care–specific framework of nonadoption, abandonment, scale-up, spread, and sustainability. Then, we drew on the results of the content analysis to create a maturity model for ML adoption in clinics according to an established development process.ResultsWith the help of the interviews, we were able to identify 13 ML-specific factors that influence the adoption process of ML systems in clinics. We categorized these factors according to 7 domains that form a holistic ML adoption framework for clinics. In addition, we created an applicable maturity model that could help practitioners assess their current state in the ML adoption process.ConclusionsMany clinics still face major problems in adopting ML systems for medical diagnostics; thus, they do not benefit from the potential of these systems. Therefore, both the ML adoption framework and the maturity model for ML systems in clinics can not only guide future research that seeks to explore the promises and challenges associated with ML systems in a medical setting but also be a practical reference point for clinicians.

Multiple representations and multi-modal reasoning in medical diagnostic systems