Evidence map›Paper›PMID 35732450›Full record

Observational studyInternal medicine (Tokyo, Japan)2023

Speech Recognition System Generates Highly Accurate Endoscopic Reports in Clinical Practice.

Hiroshi Takayama, Toshitatsu Takao, Ryo Masumura, Yoshikazu Yamaguchi, Ryo Yonezawa, Hiroya Sakaguchi, Yoshinori Morita, Takashi Toyonaga, Kazutaka Izumiyama, Yuzo Kodama

Open access · diamondAbstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in Internal medicine (Tokyo, Japan), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.3field-weighted citation impact, top 41% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 5 institutions in 1 country.

Hiroshi TakayamaDivision of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, Japan.
Toshitatsu TakaoDivision of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, Japan.
Ryo MasumuraNTT Computer and Data Science Laboratories, NTT Corporation, Japan.
Yoshikazu YamaguchiNTT Human Informatics Laboratories, NTT Corporation, Japan.
Ryo YonezawaSystem Solution Headquarters, Fujifilm Medical IT Solutions Co., Ltd, Japan.
Hiroya SakaguchiDivision of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, Japan.
Yoshinori MoritaDepartment of Gastroenterology, Kobe University Hospital International Clinical Cancer Research Center, Japan.
Takashi ToyonagaDivision of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, Japan.
Kazutaka IzumiyamaHyogo Prefecture Health Promotion Association, Japan.
Yuzo KodamaDivision of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, Japan.
Kobe University · JPNTT (Japan) · JPFujifilm (Japan) · JPHyogo Prefecture Health Foundation · JPKobe University Hospital · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective Endoscopic reports are conventionally written at the end of each procedure, and the endoscopist must complete the report from memory. To make endoscopic reporting more efficient, we developed a new speech recognition (SR) system that generates highly accurate endoscopic reports based on structured data entry. We conducted a pilot study to examine the performance of this SR system in an actual endoscopy setting with various types of background noise. Methods In this prospective observational pilot study, participants who underwent upper endoscopy with our SR system were included. The primary outcome was the correct recognition rate of the system. We compared the findings generated by the SR system with the findings in the handwritten report prepared by the endoscopist. The initial correct recognition rate, number of revisions, finding registration time, and endoscopy time were also analyzed. Results Upper endoscopy was performed in 34 patients, generating 128 findings of 22 disease names. The correct recognition rate was 100%, and the median number of revisions was 0. The median finding registration time was 2.57 [interquartile range (IQR), 2.33-2.92] seconds, and the median endoscopy time was 234 (IQR, 194-227) seconds. Conclusion The SR system demonstrated high recognition accuracy in the clinical setting. The finding registration time was extremely short.

Indexed as

Endoscopy, GastrointestinalSpeech Recognition SoftwareHumansPilot ProjectsProspective Studiesendoscopy reportrecognition ratespeech recognitionupper endoscopyvoice recognition

Identifiers

PMID35732450
PMCPMC9908383
OpenAlexW4283394493

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.