Search PubMed⌕ Search

PubMed · 9619043

[Computerized speech recognition-based endoscopic findings].

Abstract

Discrete, Hidden Markov model based speech recognition and phoneme based speech synthesis techniques were applied for gastroscopy reporting and machine control. The authors developed a special program for grammatical analysis of the sentences. Altogether 100 patient findings were grammatically analysed. The sentences were grouped according to the topographical order of the investigation: oesophagus, cardia, fundus, corpus, antrum, pylorus, bulbus, postbulbar section, and the pathological findings: erosion, ulceration, malignancy. Speech samples from 3 deep voiced male investigators were collected. The recognition rate was above 95%. A simulation program was also developed for dictation and controlling of the different equipment (monitor, printer, video, endoscope) in the gastroscopy laboratory by speech recognition. Speech synthesis was applied for the evaluation of understanding. This module artificially synthesizes the answer of the system giving backup for the understood information. With additional developments the discrete word speech 'recognition' achieved the level of routine application in medical reporting. However, ready-to-use developments need the joint activity of speech technology and endoscopy industry with end-user teams.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

B Molnár, J Gergely, L Prónai, K Papik, T Zágoni, J Fehér, L Kutor, Z Tulassay. 1998-05-17. [Computerized speech recognition-based endoscopic findings].. https://pubmed.ncbi.nlm.nih.gov/9619043/

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

KEEP EXPLORING

Related citations

Novel ultrasonic fusion imaging method based on cyclic variation in myocardial backscatter.

Quantitative ultrasonic tissue characterisation of the myocardium based on integrated backscatter (IB) has the potential of becoming an effective method for detecting and evaluating myocardial ischaemia. To facilitate IB-based clinical applications, a new imaging method has been developed that combines the anatomical information of a B-mode image with the contractile performance of a selected myocardial region. To produce such a fusion image, a region of interest (ROI) in a B-mode cardiac image was first selected by the user. Algorithms for detection of the endocardium and epicardium were developed, and the resulting mean distance between the computer-detected curve and the manually traced curve was 0.83mm for the endocardium and 0.58mm for the epicardium. The cyclic variation of IB (CVIB) of each myocardial tissue element within the ROI was then calculated over one cardiac cycle. Finally, a grey-scale B-mode image at the end of diastole was displayed as a still image, and the pixels representing the myocardial tissue in the ROI colour-coded according to the corresponding CVIB over the past heart cycle. Both the B-mode image and the colour-coded region were refreshed (up-dated) at the next end-of-diastole. Preliminary results from normal (CVIB= 10-12dB) and ischaemic (CVIB = 5-7 dB) canine hearts are presented that demonstrate the utility of this new imaging method.

Diagnosis, Computer-Assisted↗

Automated localization of nerve damage: hypothesis, technique and performance appraisal.

Needle-EMG is a common procedure performed and relied on by neurologists, physiatrists and neurosurgeons to localize focal nerve damage. However, despite its popularity, there is no standardization of this diagnostic process rendering it error prone. An automated diagnostic program may help the electromyographer by providing an objective second opinion and a statistical measure of its strength. Such a program is presented and evaluated in this manuscript. The electromyographer samples a set of muscles and grades each muscle for neuropathic changes. The Program accepts the electromyographer's graded muscle set. The Program then compares it to muscle-sets derived from all possible combinations of nerve damage and finds that combination that results in the best match. The latter is the anatomical diagnosis. Eighty-five cases from the literature, each with an impartial expert diagnosis, were analyzed using this Program. The Program precisely matched the expert electromyographers' diagnoses in 79% of the cases, partially matched in 15%, and did not match in 6% of the cases. In the latter, not necessarily the Program was in error. The Program proved to be a reliable diagnostic tool that may help electromyographers by offering a second opinion. It may also assist in teaching of residents and fellows.

Diagnosis, Computer-Assisted↗

Assessing heart rate variability from real-world Holter reports.

Real world clinical Holter reports are often difficult to interpret from a heart rate variability (HRV) perspective. In many cases HRV software is absent. Step-by-step HRV assessment from clinical Holter reports includes: making sure that there is enough usable data, assessing maximum and minimum heart rates, assessing circadian HRV from hourly average heart rates, and assessing HRV from the histogram of R-R intervals and from the plot of R-R intervals or heart rate vs. time. If HRV data are available, time domain HRV is easiest to understand and less sensitive to scanning errors. SDNN (the standard deviation of all N-N intervals in ms) and SDANN (the standard deviation of the 5-min average of N-N intervals in ms) are easily interpreted. SDNN < 70 ms post-MI is a cut point for increased mortality risk. Two times ln SDANN is a good surrogate for ln ultra low frequency power and can be compared with published cut points. SDNNIDX (the average of the standard deviations of N-N intervals for each 5-min in ms) < 30 ms is associated with increased risk in patients with congestive heart failure. RMSSD (the root mean square of successive N-N interval difference in ms) < 17.5 ms has also been associated with increased risk post-myocardial infarction. Frequency domain HRV values are often not comparable to published data. However, graphical power spectral plots can provide additional information about whether the HRV pattern is normal and can also identify some patients with obstructive sleep apnea.

Diagnosis, Computer-Assisted↗