リケラボ論文検索は、全国の大学リポジトリにある学位論文・教授論文を一括検索できる論文検索サービスです。

リケラボ 全国の大学リポジトリにある学位論文・教授論文を一括検索するならリケラボ論文検索大学・研究所にある論文を検索できる

リケラボ 全国の大学リポジトリにある学位論文・教授論文を一括検索するならリケラボ論文検索大学・研究所にある論文を検索できる

大学・研究所にある論文を検索できる 「Speech Recognition System Generates Highly Accurate Endoscopic Reports in Clinical Practice」の論文概要。リケラボ論文検索は、全国の大学リポジトリにある学位論文・教授論文を一括検索できる論文検索サービスです。

コピーが完了しました

URLをコピーしました

論文の公開元へ論文の公開元へ
書き出し

Speech Recognition System Generates Highly Accurate Endoscopic Reports in Clinical Practice

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

2023.01.15

概要

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.

この論文で使われている画像

参考文献

15.

1. Blackley SV, Huynh J, Wang L, et al. Speech recognition for

clinical documentation from 1990 to 2018: a systematic review. J

Am Med Informatics Assoc 26: 324-338, 2019.

2. Hodgson T, Coiera E. Risks and benefits of speech recognition for

clinical documentation: a systematic review. J Am Med Informatics Assoc 23: e169-e179, 2016.

3. Johnson M, Lapkin S, Long V, et al. A systematic review of

speech recognition technology in health care. BMC Med Inform

Decis Mak 14: 94, 2014.

4. Pezzullo JA, Tung GA, Rogg JM, et al. Voice recognition dictation: radiologist as transcriptionist. J Digit Imaging 21: 384-389,

2008.

5. Massey BT, Geenen JE, Hogan WJ. Evaluation of a voice recognition system for generation of therapeutic ERCP reports. Gastrointest Endosc 37: 617-620, 1991.

6. Cass OW. Automated speech technology for gastrointestinal endoscopy reporting and image recording. Proc Annu Symp Comput

Appl Med Care 968-969, 1991.

7. Molnar B, Gergely J, Toth G, et al. Development of a speechbased dialogue system for report dictation and machine control in

the endoscopic laboratory. Endoscopy 32: 58-61, 2000.

8. Yokota Y, Iwatsubo T, Takeuchi T, et al. Effects of a novel endo-

16.

17.

18.

19.

20.

scopic reporting system with voice recognition on the endoscopic

procedure time and report preparation time: propensity score

matching analysis. J Gastroenterol 57: 1-9, 2022.

Choi ES, Choi JH, Lee JM, et al. Is the environment of the endoscopy unit a reservoir of pathogens? Intest Res 12: 306, 2014.

Takao T, Masumura R, Sakauchi S, et al. New report preparation

system for endoscopic procedures using speech recognition technology. Endosc Int Open 06: E676-E687, 2018.

Masumura R, Tanaka T, Ando A, et al. Role play dialogue aware

language models based on conditional hierarchical recurrent

encoder-decoder. Proc Annu Conf Int Speech Commun Assoc Interspeech 2018: 1259-1263, 2018.

Tanaka T, Masumura R, Masataki H, et al. Neural error corrective

language models for automatic speech recognition. Proc Annu

Conf Int Speech Commun Assoc Interspeech 2018: 401-405,

2018.

de Lange T, Moum BA, Tholfsen JK, et al. Standardization and

quality of endoscopy text reports in ulcerative colitis. Endoscopy

35: 835-840, 2003.

Aabakken L. Quality reporting - finally achievable? Endoscopy

46: 188-189, 2014.

Hoff G, Ottestad PM, Skafløtten SR, et al. Quality assurance as an

integrated part of the electronic medical record - a prototype applied for colonoscopy. Scand J Gastroenterol 44: 1259-1265, 2009.

Bretthauer M, Aabakken L, Dekker E, et al. Reporting systems in

gastrointestinal endoscopy: requirements and standards facilitating

quality improvement: European Society of Gastrointestinal Endoscopy position statement. United Eur Gastroenterol J 4: 172-176,

2016.

Manfredi MA, Chauhan SS, Enestvedt BK, et al.; ASGE Technology Committee. Endoscopic electronic medical record systems.

Gastrointest Endosc 83: 29-36, 2016.

Maserat E, Safdari R, Maserat E, et al. Endoscopic electronic record: a new approach for improving management of colorectal

cancer prevention. World J Gastrointest Oncol 4: 76-81, 2012.

Gouveia-Oliveira A, Raposo VD, Salgado NC, et al. Longitudinal

comparative study on the influence of computers on reporting of

clinical data. Endoscopy 23: 334-337, 1991.

Kanda Y. Investigation of the freely available easy-to-use software

‘EZR’ for medical statistics. Bone Marrow Transplantation 48:

452-458, 2013.

The Internal Medicine is an Open Access journal distributed under the Creative

Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. To

view the details of this license, please visit (https://creativecommons.org/licenses/

by-nc-nd/4.0/).

Ⓒ 2023 The Japanese Society of Internal Medicine

Intern Med 62: 153-157, 2023

157

...

参考文献をもっと見る

全国の大学の
卒論・修論・学位論文

一発検索!

この論文の関連論文を見る