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Computer-based consultation in "care" of the critically ill patient.

Despite far-reaching progress in all areas of surgery, methods of medical data analysis and communication have not kept pace with the increased rate of data acquisition. The needs to organize and communicate these data and to provide a medium for continuing education are great in critical-care areas where the amount and the diversity of data collected are enormous, and the number of surgical team members involved in patient care has grown proportionately. The computer-based Clinical Assessment, Research, and Education System (CARE) is a time-shared computer system now available on a national basis designed to provide a management and education aid for the treatment of critically ill surgical patients. An initial clinical assessment and operative note are entered by the surgeon from which an estimation of the initial fluid, blood, and electrolyte deficits are calculated. Daily doctor's progress notes, shift nurses' summaries of vital signs, clinical information, intake and output data, and drug administration, biochemical, cardiovascular, blood gas, and respiratory information are entered for each shift. From these, a metabolic balance is calculated; fluid, electrolyte, and caloric requirements are determined; cardiorespiratory parameters are computed; and various therapuetic suggestions and cautions are given to alert the physician to problems that may be arising. The surgeon-user is assisted in making the best critical-care decisions through computer-directed, interactive prompting which focuses on the most important clinical conditions and correlations and metabolic considerations and relates the important problem to the relevant literature.

Acute Disease↗

Quantitative echocardiography: a comparison with ultrafast computed tomography in patients with chronic aortic regurgitation.

Chronic aortic regurgitation leads to progressive left ventricular dilatation and hypertrophy. It is important to be able to measure these variables for clinical decision making on the timing of aortic valve replacement. M-mode and two-dimensional echocardiography are widely utilized in clinical practice for a qualitative assessment of left ventricular size and function. Recent recommendations have been proposed for the use of quantitative assessment by left ventricular size and left ventricular mass by two-dimensional echocardiography; there has been no study examining the validity of these measurements in patients with aortic regurgitation. The purpose of this study was to prospectively examine the various geometric models of echocardiographically determined left ventricular mass and volumes in patients with chronic aortic regurgitation compared with cine-computed tomographic scanning. Twenty-two patients with chronic aortic regurgitation were prospectively identified and underwent two-dimensional echocardiographic and cine-computed tomographic scanning. M-mode and two-dimensional echocardiographic images were analyzed on an off-line measurement digital system. Seven previously described geometric models were used in this study for quantitative analysis. Both left ventricular mass and left ventricular volume were calculated from real-time ultrafast tomographic scans. Left ventricular mass determinations by two-dimensional echocardiography correlated more than M-mode determinations (r = 0.84 vs. r = 0.75, standard error of the estimate (SEE) = 38 g vs. 139 g). Biplane two-dimensional echocardiography formulas using Simpson's rule construct yielded more accurate values for left ventricular end-diastolic volumes (r = 0.92, SEE = 24 ml) than either the single-plane Simpson's rule (r = 0.58, SEE = 73 mL) or methods using predetermined models of left ventricular shape (r = 0.80, SEE = 38 ml).(ABSTRACT TRUNCATED AT 250 WORDS)

Adult↗

Expert systems.

The concept of computerized expert systems is explained, the potential utility of these systems in pharmacy is explored, and strategies and imperatives for implementing them are described. Computerized expert systems attempt a higher level of analysis than traditional computer programs. They can be defined as systems that attempt to make or assist in a decision that is not yet completely and reliably definable in objective terms. Because of the information-intensive nature of pharmacy practice, this field is particularly suited to use of expert systems. Current applications include screening for drug interactions and therapeutic drug monitoring. Expert systems must offer a substantial advantage over human expertise (for example, by quickly analyzing enormous quantities of data); those that perform functions that humans could perform have failed to gain widespread use. An ideal hospital expert system would have access to any data available about a patient's care and would detect critical situations as they occur. Such a system would require pharmacists to shift from a prescription-based orientation to a case-management orientation. Factors to consider in implementing an expert system include linkage among multiple departments, usage options, development strategies, and maintenance requirements. Computerized expert systems hold great potential for application to pharmacy and may influence the pharmacist's role in patient care.

Drug Interactions↗

Creating care plans via modems: using a hospital information system in nursing education.

Preparing nursing students for the demands of high technology in health professions is a challenge for everyone involved in nursing education today. Advancements in technology have changed the health care delivery system resulting in computerization that affects all areas of patient care. Nursing education programs must assure that all graduates can effectively use computer technology. To address this need, the Wright State University-Miami Valley School of Nursing was one of the first schools in the nation to provide connectivity via computers in the school's student computer lab to a real hospital information system. This connectivity allows the nursing students to create care plans for their assigned patients, plans which after discussion with the instructor and the patients' nurse, are entered into the patients' records. This real-life application allows the students to see not only the results of their decision-making, but the overall process of patient care. The benefits of having students use a nursing information management system include enhanced student motivation, professional socialization, the ability to understand the "whole clinical picture," and decreased fear of computer technology. The computers in the student computer lab play an invaluable role in the education of the nursing students. They give the students the opportunity to experience a real-life application in the nursing profession and prepare them for the world of technology they will encounter after graduation.

Computer Security↗

Computerized detection of arterial oxygen desaturations in an intensive care unit.

Automatic detection of arterial oxygen desaturations was investigated by collecting pulse oximeter saturation data through an MIB. Two algorithms, one based on a threshold principle and the other based on moving median calculations, performed the detection. The median algorithm detected fewer "unimportant" events than did the threshold algorithm, but also did not detect some "important" events that the threshold algorithm detected. Successful detection algorithms will likely need to incorporate into their decision-making other patient information in addition to saturation. A proposed recording algorithm is described.

Algorithms↗

Imaging using solid-state detectors.

Whether they know it or not, many dentists already have adapted solid-state imaging devices into their offices in the form of an intraoral or video-camera system for esthetic dentistry and patient education. These dentists own charge-coupled devices. The technology, however, had been accepted well before instruction in the imaging principles was available. With the addition of digital radiographic systems, the dentist who is "buying into" these contemporary imaging devices must become familiar with the imaging principles behind them, and with the applications of these systems to dental problems or tasks. This article attempts to bring the dentist "up to speed" with this technology and discusses its present and future applications. In this way, the clinician will move past the simple "show-and-tell" stage or patient education and begin to see the true potential of imaging for more accurate detection of disease, for quantitating changes in that disease through time, and for making better clinical decisions about patient treatment.

Computer Systems↗

[Computer simulation of a patient with gastrointestinal symptoms used for evaluation of clinical decision among general practitioners].

This study used a computer-based simulation of a patient with gastrointestinal symptoms for an investigation of decision-making amongst 122 general practitioners participating in the 6th Nordic Congress of General Practice. The simulation model, developed in the computer software COMCATS, makes use of an IBM-compatible personal computer attached to an automated slide-projector. All participants were given a short introduction to the patient and his symptoms, but all further information was optimal and selected by the doctor. Large variations were found between the participants with respect to clinical patterns: for example, expenses for further examinations ranged from 0 to 2,400 Danish crowns. For research into clinical decision-making the great advantages of the computer-model are that it allows for a standardisation of the clinical situation, gives the doctor an opportunity to select between optional information and permits an automatic gathering of huge amounts of information about the decision-making process.

Adult↗

Computer-based patient education: a progress report.

Computer-based teaching can help patients make informed decisions and protect both patients and physicians from the serious consequences of poor communication. This article discusses the benefits of computer-based patient education and its role in the emerging field of "patient informatics."

Communication Barriers↗

Development of nursing CAI in the administration: nursing daily hospital problems.

Since the nurse emerged as a qualified professional to attend human beings, the role which always characterized his/her action was assistance. Already since the foundation of the first nursing school, teaching aimed at developing the student to dispense nursing care necessary for the patient's assistance. When the nurse started to perform their activities in nosocomial and community institutions, the need for someone who would be responsible for the organization, coordination and control of nursing personnel and the Nursing Unit itself, in short, someone who would head the services became apparent. At present, the professional role of the nurse is changing regarding his/her overall assistance to the patient, caring directly, determining and/or carrying out nursing assistance necessary for the patient's health, maintenance or recovery. In this manner, it is necessary for nurses to use tools of administration science such as planning, decision making, and planned changes in order to be able to provide the nursing assistance with quality. Thus, this study aims at helping the development of the nurse's interest in computer during the teaching-learning process, motivating the search for knowledge in computerized sources. We intended to offer the opportunity for discussion and reflection on problems of nosocomial nursing practice, favoring interchange of theory and practice. The system is being developing using ToolBook Live Software to be run in Windows environment. The content of the subject allow the user (student or professional nursing) to choose the most correct answers to questions in the knowledge area. In this case, to discuss nursing administration issues through the study of practical situations is possible, such as the opportunity the verify the performance in tests.

Brazil↗

Computer-assisted trauma care prototype.

Each year, civilian accidental injury results in 150,000 deaths and 400,000 permanent disabilities in the United States alone. The timely creation of and access to dynamically updated trauma patient information at the point of injury is critical to improving the state of care. Such information is often non-existent, incomplete, or inaccurate, resulting in less than adequate treatment by medics and the loss of precious time by medical personnel at the hospital or battalion aid station as they attempt to reassess and treat the patient. The Trauma Care Information Management System (TCIMS) is a prototype system for facilitating information flow and patient processing decisions in the difficult circumstances of civilian and military trauma care activities. The program is jointly supported by the United States Advanced Research Projects Agency (ARPA) and a consortium of universities, medical centers, and private companies. The authors' focus has been the human-computer interface for the system. We are attempting to make TCIMS powerful in the functions it delivers to its users in the field while also making it easy to understand and operate. To develop such a usable system, an approach known as user-centered design is being followed. Medical personnel themselves are collaborating with the authors in its needs analysis, design, and evaluation. Specifically, the prototype being demonstrated was designed through observation of actual civilian trauma care episodes, military trauma care exercises onboard a hospital ship, interviews with civilian and military trauma care providers, repeated evaluation of evolving prototypes by potential users, and study of the literature on trauma care and human factors engineering. This presentation at MedInfo '95 is still another avenue for soliciting guidance from medical information system experts and users. The outcome of this process is a system that provides the functions trauma care personnel desire in a manner that can be easily and accurately used in urban, rural, and military field settings. his demonstration will focus on the user interfaces for the hand-held computer device included in TCIMS, the Field Medic Associate (FMA). The FMA prototype is a ruggedized, water-resistant personal computer, weighing approximately 5 lbs. It has an LCD graphical user interface display for patient record input and output, pen-based and audio input, audio output, and wireless communications capabilities. Automatic recording and dynamic, graphical display of time-stamped trends in patient vital signs will be simulated during the demonstration. Means for accessing existing patient record information (e.g., allergies to particular medications) and updating the record with the nature of the injury, its cause, and the treatments that were administered will be shown. These will include use of an electronic pen to mark up anatoglyphs (standard drawings of human body appearing on computer screen) to show where injuries occurred and where treatments were applied, and to input textual descriptions of the nature of the injury, its cause, what treatments were administered, etc. Computer recognition of handwritten inputs will be shown. Likewise, voice annotation and audio playback of patient record information by medics and hospital personnel will be illustrated. These latter technologies free the care providers' hands to treat the patient; they can therefore provide inputs to the patient record while information is fresh in their minds. The audio playback option allows hospital personnel to select more detailed voice annotations of specific portions of the patient record by simply touching the electronic pen to a particular place where an electronic pen marking was made by a medic in the field and then listening to the medic's corresponding audio commentary. Finally, the FMA's means for assisting the medic in simultaneously managing several injured patients will be shown. (abstract truncated)

Emergency Medical Services↗

ROSE: decision trees, automatic learning and their applications in cardiac medicine.

Computerized information systems, especially decision support systems, have acquired an increasingly important role in medical applications, particularly in those where important decisions must be made effectively and reliably. But the possibility of using computers in medical decision making is limited by many difficulties, including the complexity of conventional computer languages, methodologies, and tools. Thus a conceptual simple decision making model with the possibility of automating learning should be used. In this paper, we introduce a cardiological knowledge-based system based on the decision tree approach supporting the mitral valve prolapse determination. Prolapse is defined as the displacement of a bodily part from its normal position. The term mitral valve prolapse (PMV), therefore, implies that the mitral leaflets are displaced relative to some structure, generally taken to be the mitral annulus. The implications of the PMV are: disturbed normal laminar blood flow, turbulence of the blood flow, injury of the chordae tendinae, the possibility of thrombus's composition, bacterial endocarditis, and, finally, hemodynamic changes defined as mitral insufficiency and mitral regurgitation. Uncertainty persists about how it should be diagnosed and about its clinical importance. It is our deep belief that the echocardiography enables properly trained expert armed with proper criteria to evaluate PMV almost 100%. But, unfortunately, there are some problems concerned with the use of echocardiography. With this in mind, we have decided to start a research project aimed at finding new criteria and enabling the general practitioner to evaluate the PMV using conventional methods and to select potential patients from the general population. To empower doctors to perform needed activities, we have developed a computer tool called ROSE (computeRized prOlaps Syndrome dEtermination) based on algorithms of automatic learning. This tool supports the definition of new criteria and the selection of potential PMV-patients. The ROSE is based on concepts of decision trees and automatic learning. The decisions and learning process can be presented with an easily visualized two dimensional model; thus decision trees are straightforward to build and interpret. Decision trees use different object attributes to classify different subsets of objects. (Their great advantage is that they don't use a fixed number of predetermined attributes.) In the decision tree approach, the members of a set of objects are classified as either positive or negative instances (in our case patients with PMV Syndrome or without it). Candidate attributes that may possibly describe the concept are then outlined. A decision tree construction tool uses outlined attributes to formulate the appropriate decision tree that identifies all positive instances of the underlying concept according to objects with known classification. (In our case the classification is done with the echo examination). The first set of objects used for the tree generation is usually called the training set. This decision tree characterization next becomes a basis: 1) forecasting whether an object previously unseen is a positive or negative instance of the concept being modeled, and 2) the hierarchical representation of the most important attributes of the concept being investigated. Our main interest and idea is to discover symptoms, syndromes, and illnesses related to PMV that can be distinguished by general practitioners in their everyday job and which should help them to identify possible PMV candidate patients. To this end, we constructed a computerized tool called ROSE. According to the principles presented above, we first taught ROSE using the sample of 400 examined volunteers. ROSE, considering that the clinical PMV diagnosis is practically not researched, is relatively successful. (abstract truncated)

Algorithms↗

Simulation in computer of the mechanical ventilator Servo 900C made by Siemens Elema.

In our country, Intensive Care Units (ICU) for both adults and children include medical equipment of high technology that saves the lives of patients in critical condition. Many of these patients for different pathologies need the use of a mechanical ventilator which keeps the patient's respiration and allows the medical staff to assist them with other means. In practice, there are a lot of different situations that the medical staff have to face and make adequate decisions that will help save the patients' lives. In this case, the mechanical ventilator must be used, requiring a great deal of experience acquired along many years of work. The mechanical ventilator SERVO 900C made by Siemens Elema is available in all ICUs; this equipment is used for the treatment of more critical patients due to his reliability and technical features. Based on this fact, a simulator was designed to help train the less experienced staff in the use of the ventilator and to allow them to face real case simulations, making decisions that will be adequate or wrong with no risk to the patient's life. In this way, they would acquire the necessary knowledge on how to use the equipment in real-life situations. The created system displays the control panel of the ventilator allowing the user to interact. The control panel includes both analogical and digital devices that show different parameters and also the gases mixer which is connected to the equipment. During the work, the user can access a calculator to facilitate the adjustment of some parameters in case any calculation is needed. The system is divided into modules. The module "Exercises" permits you to choose one out of a set and solve it by means of the adjustment of the controls; this answer is analyzed and warning messages are displayed in case a control has been set incorrectly. The aim of these exercises is that the users learn how to handle the equipment by parts and are conceived for a sequence of question-answer. The module "Problems" describes the condition of a given patient which must be treated with the ventilator. The user will then simulate his performance in the adjustment of it. The sequence question-answer will change dynamically according to the evolution previously previewed for the patient by the professor. The simulator has a set of acoustic and visual alarms which are activated during the occurrence of different anomalies. These alarms work according to the professor's will at the moment of planning the lesson. The module "Information" gives the user the possibility of consulting topics about the equipment such as: functions, patient's safety, installation, clinical judgment and location of failures. In the last module "Options" the user defines the files (exercises or problems) he is going to use as well as the directory of work and the initialization file. The system uses pull-down menus, providing a context-sensitive on screen help with information about the function of each control and how to handle it. For the preparation of the files of exercises and problems only Servo editor can be used; this editor works independently from the simulator. The system requires a PC IBM Compatible with 640 kb RAM, MS-DOS operating system, and VGA color display. We consider that with the implementation this system the user can gain experience and knowledge about the use of the ventilator before he faces real situations.

Adult↗

Balancing learner control and realism with specific instructional goals: case studies in fluid balance for nursing students.

In designing instruction, there is an inherent tension between permitting learner control and focusing on specific instructional goals. This tension is particularly problematic in computer-based clinical case studies. In addition, the question of the appropriate level of realistic detail is central to case study design. More learner control and greater detail require greater complexity and interactivity, and consequently longer development time per case. They may also lead to difficulty in focusing on specific content. This program is designed to present computer-based case studies as an adjunct to classroom instruction in a specific content area, with significant compromises in realism compensated by the potential to focus on a specific set of problems and expose students to a fairly large number of "patients." The degree of learner control is necessarily limited by the program's structure, but the design attempts to allow a considerable amount of self-direction within those limits. The program's main path goes through a series of case studies illustrating a variety of fluid and electrolyte problems. These case studies are fairly simple and tightly structured: history, physical assessment, and laboratory data are presented near the beginning of each study, and learners are given predefined choices with immediate feedback at specified decision points. The case studies are designed to be completed fairly rapidly, typically no more than about thirty minutes per case. The distortion of the case study format by the obvious focus on fluid and electrolyte problems is compensated somewhat by inclusion of a number of cases with no relevant problems. Within this structured format, the program gives the learner control over a number of aspects of the learning experience. Hypertext is used hierarchically, with two levels of explanation for some central concepts and one level for material not directly relevant to the main instructional goals. Consequently, the learner controls content density to a considerable degree. Since nursing students are under pressure to absorb large amounts of clinically relevant information within a relatively short time, the availability of additional information on diagnoses, charting abbreviations, normal ranges for lab values, etc., may provide an added incentive to use the program. Though the case studies are linear with no branching, learners have several options in using them. The basic user mode involves simply going through the case studies and "solving" them: evaluating the data, identifying problems, and making basic treatment decisions. Alternatively, the learner may choose a particular problem and use the program as a "semi-tutorial." Backward movement is allowed, either to review the development of a case or to try again after an incorrect response. Accumulated information is available in an on-screen "chart" for ready reference. A "reference library" is accessible for browsing from any point in the program. Learner evaluation of the program is in progress.

Computer Simulation↗

Evaluation of partial classification algorithms using ROC curves.

When using computer programs for decision support in clinical routine, an assessment or a comparison of the underlying classification algorithms is essential. In classical (forced) classification, the classification rule always selects exactly one alternative. A number of proven discriminant measures are available here, e.g.sensitivity and error rate. For probabilistic classification, a series of additional measures has been developed [1]. However, for many clinical applications, there are models where an observation is classified into several classes (partial classification), e.g., models from artificial intelligence, decision analysis, or fuzzy set theory. In partial classification, the discriminatory ability (Murphy) can be adjusted a priori to any level, in most practical cases. Here the usual measures do not apply. We investigate the preconditions for assessment and comparison based on medical decision theory. We focus on problems in the medical domain and establish a methodological framework. When using partial classification procedures, a ROC analysis in the classical sense is no longer appropriate. In forced classification for two classes, the problem is to find a cutoff point on the ROC curve; while in partial classification, you have to find two of them. They characterize the elements being classified as coming from both classes. This extends to several classes. We propose measures corresponding to the usual discriminant measures for forced classification (e.g., sensitivity and error rate) and demonstrate the effects using the ROC approach. For this purpose, we extend the existing method for forced classification in a mathematically sound manner. Algorithms for the construction of thresholds can easily be adapted. Two specific measurement models, based on parametric and non-parametric approaches, will be introduced. The basic methodology is suitable for all partial classification problems, whereas the extended ROC analysis assumes a rank order of the selected alternatives. The method is based on the black box principle and makes use only of the results of the compared algorithms and some general intuitive principles. Therefore, algorithms arising from different "philosophical" approaches may also be compared according to their results. For demonstration purposes, a clinical example from cranial computed tomography is presented. Linear discriminant analysis as a reference is compared to a Bayesian and a fuzzy procedure.

Algorithms↗

Computer-aided clinical laboratory diagnosis in conjunction with the electronic medical textbook.

1. INTRODUCTION. Medical knowledge has been increasing and diversifying on a worldwide scale, while the specialization of physicians has been extended vigorously. Under this environment, it may be natural that mistakes are made in a comprehensive diagnosis, as the physician cannot master all of this dramatically increasing volume of knowledge. The knowledge has extended beyond the memory of human beings, thereby causing the deterioration of service; this is called the "Knowledge crisis." To tackle this problem, the Electronic Medical Textbook (EMT) has been conceived and set up as a medical knowledge base for physicians to optimize both their specialties and activities in clinical practice. Meanwhile, laboratory information systems were widely introduced. However, there are few systems which allow interpretation of the findings obtained. With this in mind, we have improved the utility of the EMT by enhancing its function with laboratory information follow-up, thesaurus back-up, Japanese language support, and online access. 2. SYSTEM DESCRIPTION. The knowledge database for medical decision-making consists of three categories: 1) Medical domain knowledge (including approximately 3500 diseases) from the AMA's "The Current Medical Information & Terminology (CMIT)"; 2) Knowledge on relationship between laboratory testing results and diseases from "The Effects of Disease on Clinical Laboratory," compiled by the AACC; and 3) Clinical testing knowledge from Otsuka's laboratory test handbook "Kensa-Kojien." These categories are connected by links in the process of cross-reference. In actual use, the first is to select the supporting system bringing up clinical signs and findings on CRT from which users can then choose any representations corresponding to the patient's clinical state. Once the relevant objects have been selected, the system presents the correlated investigative tests to be performed, along with a scope of laboratory tests ordering for its initial investigative task. The required tests against the data objects denoted by "High," "Low," or "Abnormal" are linked to possible diagnoses, which appear on CRT in order of likelihood. Similarly, the possible diagnoses can be obtained directly by consulting the patient's laboratory test results in linkage, by way of automated data transformation into the corresponding data object using the reference interval. If necessary, additional information relative to clinical signs can be added. With the repetition of this procedure, clinically useful tests can be located, thus increasing the likelihood of an appropriate diagnosis. The proposed diseases can be confirmed through cross-reference to the patient's clinical signs from the description in the Textbook on CRT. In order to bolster the efficiency of cross-checking on CRT, the sentences including hit-words are highlighted in red. Moreover, with regard to specific keywords for disease, those sentences are highlighted in green to emphasize differences in between. In parallel, the description points out an active behavior of laboratory test results in the progression of the disease and is also able to display applicable laboratory test listings. Furthermore, this knowledge-based interpretation system is directly accessible via online network services in referring to laboratory test results. 3. CONCLUSION. A combination of the CMIT and laboratory diagnostic information in the knowledge database allows the narrowing down of proposed diagnoses due to its flexible approach, which depends not only on clinical signs and findings, but also on applicable test-ordering support and suggestions. This enhances the potential usefulness of the EMT. The system contributes to the support of the diagnostic procedures and assists in educational setting, because the process can be operated in reverse based on clinical signs and laboratory findings in a dialogue style on CRT. The related disease description highlighted in color has won a good reputation dur

Artificial Intelligence↗

Image processing and computer-aided diagnosis.

The future of image processing and CAD in diagnostic radiology is more promising now than ever, with increasingly impressive results being reported from various observer performance studies in both mammography and chest radiography. Clinical trials in years to come will help optimize the accuracy of the programs and determine the actual contribution of CAD to the interpretation process. Radiologists using output from computer analyses of images, however, will still make the final decision regarding diagnosis and patient management. Nonetheless, studies have indicated that the computer output need not have greater overall accuracy than a given radiologist in order to improve his or her performance. A systematic and gradual introduction of CAD into radiology departments will be necessary so that radiologists can become familiar with the strengths and weaknesses of each CAD program, thereby avoiding either excessive reliance or a dismissive attitude toward the computer output. This should ensure the acceptance of CAD and optimal diagnostic performance by the radiologist. Thus, an appropriate role for each CAD program will be determined for each radiologist, according to his or her individual training and observational skills, reducing intraobserver variations and improving diagnostic performance.

Diagnosis, Computer-Assisted↗

[Imaging science and technology in diagnostic radiology: expectations in the second century of Roentgen's discovery of X-rays].

During the first century since the discovery of X-rays by Roentgen in 1885, imaging technology has contributed significantly to the progress of diagnostic radiology, mainly by providing various methods and techniques for production of diagnostic images. In the next century, it is expected that imaging science and technology will contribute to the most important process of diagnostic decision makings by radiologists and physicians by providing quantitative analyses of medical images using high-speed computers. The computer output may be used as a "second opinion" to assist radiologists' interpretation of images. This concept has been investigated as computer-aided diagnosis (CAD) during the last ten years in chest radiography and mammography for detection of lesions and characterization of normal and abnormal patterns. The aim of CAD is to improve the accuracy and the consistency of radiologic diagnoses. In this article, recent results are presented on the detection of lung nodules and pneumothoraces as well as quantitative analyses of interstitial infiltrates and cardiomegaly in chest radiographs. In mammography, CAD schemes are being developed for detection of clustered microcalcifications and masses. Recently, the prototype mammography intelligent workstation has been implemented in the clinical section of our department and initial clinical results from screening cases appear promising.

Diagnosis, Computer-Assisted↗

Functional magnetic resonance imaging for intracranial navigation.

Functional MR imaging can provide accurate anatomic and physiologic localization of human cortical function. This new method of noninvasive cortical mapping appears to be valuable preoperatively for risk assessment, therapeutic decision making and surgical planning. The integrated volume rendering of brain surface topography, cortical veins, structural lesion, and sites of functional activation is also useful intraoperatively for defining cortical resection boundaries in patients with lesions in critical areas.

Adolescent↗