Search PubMed⌕ Search
Home/Archive/PubMed

PubMed · 10299304

A microprogrammed data acquisition system for renography.

C Imperiale
View original source ↗Export metadata ↓

Abstract

The purpose of this project was to design an efficient, low cost, and portable system for renography suitable for clinical use. The principles involved in the renographic test, and the procedures and calculations which act on the design of our system, are given. The system consists of an Apple II Plus computer equipped with 48K memory, two disk drives with diskettes of 143K each, a thermal printer with graphic capability, the Microsoft Z80 card, and an interface which is specifically designed for renographic data acquisition.

Explore related subjects

Diagnosis, Computer-AssistedRadioisotope RenographyJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

C Imperiale. A microprogrammed data acquisition system for renography.. https://doi.org/10.1097/00004669-198307000-00012

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2000

[The operating room of the future].

Advances in computer technology will revolutionize surgical techniques in the next decade. The operating room (OR) of the future will be connected with a laboratory where clinical specialists and researchers prepare image-guided interventions and explore the possibilities of these techniques. The virtual reality is linked to the actual situation in the OR with the aid of navigation instruments. During complicated operations the images prepared preoperatively will be corrected during the operation on the basis of the information obtained peroperatively. MRI currently offers maximal possibilities for image-guided surgery of soft tissues. Simpler techniques such as fluoroscopy and echography will become increasingly integrated in computer-assisted peroperative navigation. The development of medical robot systems will make possible microsurgical procedures by the endoscopic route. Tele-manipulation systems will also play a part in the training of surgeons. Design and construction of the OR will be adapted to the surgical technology, and include an information and control unit where preoperative and peroperative data come together and from where the surgeon operates the instruments. Concepts for the future OR should be regularly adjusted to allow for new surgical technology.

Diagnosis, Computer-Assisted↗
PubMed2000

Visions of clinical assessment: then, now, and a brief history of the future.

Perspectives on the discipline of psychological assessment are provided through reflections on a classic article by Hunt (1946), review of current issues, and a projection of changes likely to occur over the next fifty years. Topics covered include the place of idiosyncratic qualitative responses, test design, clinical judgment, managed care, financial efficacy, over emphasis on pathology, treatment planning, status of projectives, ecological validity, relationship between theory and tests, innovations in scale construction, and computer-assisted assessment. Future predictions relate to innovations in computer-assisted assessment, practitioner roles in an increasingly automated environment, the future of traditional tests, and human-rights issues.

Diagnosis, Computer-Assisted↗
PubMed2000

Using an artificial neural network to detect activations during ventricular fibrillation.

Ventricular fibrillation is a cardiac arrhythmia that can result in sudden death. Understanding and treatment of this disorder would be improved if patterns of electrical activation could be accurately identified and studied during fibrillation. A feedforward artificial neural network using backpropagation was trained with the Rule-Based Method and the Current Source Density Method to identify cardiac tissue activation during fibrillation. Another feedforward artificial neural network that used backpropagation was trained with data preprocessed by those methods and the Transmembrane Current Method. Staged training, a new method that uses different sets of training examples in different stages, was used to improve the ability of the artificial neural networks to detect activation. Both artificial neural networks were able to correctly classify more than 92% of new test examples. The performance of both artificial neural networks improved when staged training was used. Thus, artificial neural networks may beuseful for identifying activation during ventricular fibrillation.

Diagnosis, Computer-Assisted↗