Awake intubation of the adult trachea using the Bullard laryngoscope.

Awake intubation using the Bullard laryngoscope can be comfortably and easily performed in the adult. Five cases are presented in which tracheal intubation was performed under topical anaesthesia with light intravenous sedation. In each case, topical anaesthesia was performed by insertion of a Guedel oral airway, with lidocaine ointment applied to the inferior and posterior surfaces. In one case, Bullard intubation was successful where direct laryngoscopy and multiple attempts at bronchoscopic intubation by three different operators had failed. We conclude that the Bullard laryngoscope can be easily used in awake patients and may be a useful alternative where other methods for awake intubation have failed.

Knowledge-based radiologic image retrieval using axes of clinical relevance.

This paper describes an approach to computer-based intelligent retrieval of feature-coded radiographic images relevant to a specific case being evaluated. The approach involves partitioning the search space along clinically natural groups of attributes which we call "axes of clinical relevance." By embedding knowledge about the domain to help direct the search process, a clinician's needs may be met more comprehensively. Domain knowledge, supplied to the system as "axis heuristics," may make search more robust. These heuristics provide a graded, progressive relaxation of the search constraints. This approach helps show the user groups of images in order of probable relevance to a current case. AXON is a prototype knowledge-based system constructed to illustrate this approach in the domain of chest imaging. This paper describes the AXON system, demonstrates some searches which illustrate the potential utility of this approach, and discusses preliminary tests of the search strategies used by AXON.

A parallel software architecture for building intelligent medical monitors.

Intensive care units become more complicated each day as the number of devices developed to monitor various aspects of a patient's status continues to increase. Intelligent monitors attempt to reduce this complexity by interpreting the data and presenting a high level summary to the clinician. We propose an innovative parallel software architecture for constructing intelligent medical monitors: the process trellis. The process trellis is an explicitly parallel structure, and therefore can take advantage of the performance gains available from parallel computing hardware. It does not, however, presuppose any expertise in parallel programming on the part of the application programmer. A prototype cardiovascular monitor has been built using this parallel software architecture. Preliminary testing of the monitor has shown that real-time cardiovascular monitoring, including data calculations, symbolic classification, and interpretation can be accomplished in real-time.

DYNASCENE: an approach to computer-based intelligent cardiovascular monitoring using sequential clinical "scenes".

Hemodynamic abnormalities such as hypovolemia typically progress through a sequence of discrete clinical phases or "scenes" (e.g., intravascular volume depletion, vasoconstriction, hypotension). Each scene can be defined by a cluster of hemodynamic trends. A natural approach to modeling the process of hemodynamic monitoring involves identifying these scenes and the temporal relationships among them. This approach has been utilized in the development of DYNASCENE, a parallel programming implementation of a computer-based intelligent hemodynamic monitor. This paper discusses: (1) The rationale for utilizing sequential clinical scenes to represent knowledge of hemodynamic behavior, (2) the design of the DYNASCENE system, and (3) preliminary tests of the DYNASCENE system.

Expert system-controlled image display.

Conventional computer-based medical expert systems deliver advice to physicians as written text. While such advice is useful, it has distinct limitations in a visually oriented discipline such as diagnostic radiology, in which decisions often depend on pattern recognition and appreciation of subtle morphologic features. The authors developed a prototype expert computer system, IMAGE/ICON, which displays groups of images sorted into a series of axes based on different ways in which they may be similar. They may share a common feature, group of features, causes, or clinical setting. IMAGE/ICON may display examples of morphologic variations of a dominant finding or a spectrum of abnormalities seen in an specific disease or group of diseases. The system also assembles a written analysis of key features of a case. Such a tool may be useful as a diagnostic aid or for continuing medical education. It is likely to have particular impact in the form of an intelligent radiologic workstation, as picture archiving and communication systems become available.

Acoustic neurinoma diagnostic model evaluation using decision support systems.

Three acoustic neurinoma (hereafter called acoustic neuroma) diagnostic models (Jenkins, Le Liever, Kaseff) were implemented as rule-based decision support systems and evaluated from the perspective of sensitivity, specificity, and US dollar cost, using a data base of 95 case histories suggestive of acoustic neuroma. The specificities of the models were equivalent (.97). The Jenkins model had the highest sensitivity (.96) and the highest average cost ($1470.99). The sensitivities and average costs of the Le Liever and Kaseff models were comparable (.84 vs. .82, and $1092.38 vs $1114.17, respectively). We observed that omitting brain-stem evoked response and electronystagmography testing from the Le Liever model subjected four (4.2%) more patients without acoustic neuroma to air contrast computed tomography, increased sensitivity to .89, and decreased the average cost to $774.75, without affecting specificity. We discuss the reasons for the slightly improved sensitivity and the impact of decision support systems on the clinician.