Evidence map›Paper›PMID 38097750›Full record

ArticleSurgical endoscopy2024

Development and evaluation of a program based on a generative pre-trained transformer model from a public natural language processing platform for efficiency enhancement in post-procedural quality control of esophageal endoscopic submucosal dissection.

Huaiyuan Ma, Xingbin Ma, Chunxiao Yang, Qiong Niu, Tao Gao, Chengxia Liu, Yan Chen

Abstract read
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In one paragraph

Article in Surgical endoscopy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
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3citing papers in PubMed, 1 pooled it
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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3 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Huaiyuan MaDepartment of Gastroenterology and Hepatology, Binzhou Medical University Hospital, Binzhou, 256603, Shandong, China.
Xingbin MaDepartment of Gastroenterology and Hepatology, Binzhou Medical University Hospital, Binzhou, 256603, Shandong, China.
Chunxiao YangDepartment of Gastroenterology and Hepatology, Binzhou Medical University Hospital, Binzhou, 256603, Shandong, China.
Qiong NiuDepartment of Gastroenterology and Hepatology, Binzhou Medical University Hospital, Binzhou, 256603, Shandong, China.
Tao GaoEndoscopy Center of Binzhou Medical University Hospital, Binzhou, Shandong, China.
Chengxia LiuDepartment of Gastroenterology and Hepatology, Binzhou Medical University Hospital, Binzhou, 256603, Shandong, China. phdlcx@163.com.
Yan ChenDepartment of Gastroenterology and Hepatology, Binzhou Medical University Hospital, Binzhou, 256603, Shandong, China. chenyanfeihong0906@163.com.

Funding

Foundation for Provincial Clinical Key Specialty Development of Shandong Province SLCZDZK-13Foundation for Provincial Clinical Key Specialty Development of Shandong Province SLCZDZK-25
6 · The paper itself

Abstract

backgroundPost-procedural quality control of endoscopic submucosal dissection (ESD) is emphasized in guidelines. However, this process can be tedious and time-consuming. Recently, a pre-training model called generative pre-trained transformer (GPT) on a public natural language processing platform has emerged and garnered significant attention, whose capabilities align well with the post-procedural quality control process and have the potential to streamline it. Therefore, we developed a simple program utilizing this platform and evaluated its performance.

methodsEsophageal ESDs were retrospectively included. The manual quality control process was performed and act as reference standard. GPT's prompt was optimized through multiple iterations. A Python program was developed to automatically submit prompt with pathological report of each ESD procedure and collect quality control information provided by GPT. Its performance on quality control was evaluated with accuracy, precision, recall, and F-1 score.

results165 cases were involved into the dataset, of which 5 were utilized as the prompt optimization dataset and 160 as the validation dataset. Definitive prompt was achieved through seven iterations. Time spent on the validation dataset by GPT was 13.47 ± 2.43 min. Accuracies of pathological diagnosis, invasion depth, horizontal margin, vertical margin, vascular invasion, and lymphatic invasion of the quality control program were (0.940, 0.952) (95% CI), (0.925, 0.945) (95% CI), 0.931, 1.0, and 1.0, respectively. Precisions were (0.965, 0.969) (95% CI), (0.934, 0.954) (95% CI), and 0.957 for pathological diagnosis, invasion depth, and horizontal margin, respectively. Recalls were (0.940, 0.952) (95% CI), (0.925, 0.945) (95% CI), and 0.931 for factors as mentioned, respectively. F1-score were (0.945, 0.957) (95% CI), (0.928, 0.948) (95% CI), and 0.941 for factors as mentioned, respectively.

conclusionsThis quality control program was qualified of post-procedural quality control of esophageal ESDs. GPT can be easily applied to this quality control process and reduce workload of the endoscopists.

Indexed as

Endoscopic Mucosal ResectionEsophageal NeoplasmsHumansNatural Language ProcessingQuality ControlRetrospective StudiesArtificial IntelligenceEndoscopic submucosal dissectionEsophagusNatural language processingQuality control

Identifiers

PMID38097750

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