PD08.07. Multimodal AI-Driven Postoperative Surveillance for Oesophageal Cancer: Automated Patient-Reported Outcome Assessment and Early Complication Warning in a Prospective Study

Clicks: 1
ID: 325700
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #74 of 453 articles by views in diseases of the esophagus : official journal of the international society for diseases of the esophagus

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 453 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Abstract Topic Esophageal Cancer: Surgical Treatment of Esophageal Cancer – early outcomes and complications Background Postoperative complications following oesophagectomy occur in 25–60% of patients, yet traditional patient-reported outcome (PRO) assessment relies on standardised questionnaires with limited reliability, particularly among elderly patients with low health literacy. We aimed to develop and externally validate a multimodal artificial intelligence (AI) system that automates PRO assessment from natural patient conversations and provides early warning of major postoperative complications. Methods We conducted a prospective observational study at a tertiary cancer center, enrolling consecutive patients undergoing esophagectomy.The development cohort (Centre 1, n=196) and the temporally independent external validation cohort (Centre 2, n=103) were recruited sequentially. Using the validated Patient Symptom Assessment for Oesophageal Cancer (PSA-ESO) instrument, we collected trimodal recordings (video, audio, text) at up to 25 timepoints per patient, yielding 6,813 evaluable assessments. We fine-tuned the Qwen2.5-Omni-7B multimodal large language model with LoRA adaptation for two tasks: automated PRO symptom grading (Task A) and Temporal Transformer-based early warning of Clavien-Dindo grade II or higher complications (Task B). In a prospective implementation substudy (n=62), we evaluated the clinical impact of real-time alerts on time-to-intervention. Results In the external validation cohort, the trimodal PRO assessment achieved a weighted kappa of 0.801 (95% CI 0.73–0.87) and ICC of 0.858 against expert consensus, significantly outperforming audio-text bimodal (kappa 0.754, p<0.0001) and text-only (kappa 0.689, p<0.0001) configurations. The early warning model achieved an AUROC of 0.873 (95% CI 0.82–0.93) with a mean detection lead time of 28.7 hours before clinical diagnosis. The system detected 76.1% of symptom under-reporting cases. In the implementation substudy, real-time alerts reduced median time-to-intervention from 14.2 hours to 6.8 hours (p=0.003) and were associated with shorter ICU stays (3.1 vs 5.4 days, p=0.028). Conclusion An end-to-end multimodal AI system can accurately automate PRO assessment from natural patient conversations and provide clinically meaningful early warning for postoperative complications, with external validation confirming generalisability across cohorts. This conversation-based approach represents a paradigm shift from questionnaire-based PRO evaluation in surgical oncology, with particular relevance for populations with limited health literacy.
Reference Key
openalex_W7203864221 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Simiao Lu, Yi Zhu, Yongtao Han, Qiuling Shi, Xuefeng Leng
Journal diseases of the esophagus : official journal of the international society for diseases of the esophagus
Year 2026
DOI
10.1093/dote/doag077.138
URL
https://doi.org/10.1093/dote/doag077.138
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.