Clinical Report: Semi-Automated NLP-Based Generation of FESS Surgical Reports

Overview

This proof-of-concept study demonstrates the feasibility of using a neural language model to semi-automatically generate coherent and detailed functional endoscopic sinus surgery (FESS) reports from intraoperative keywords. The model was trained on 48 anonymized conventional reports and 1500 sentences, showing potential to reduce physician workload and optimize clinical workflows.

Background

Artificial intelligence (AI), particularly machine learning techniques such as natural language processing (NLP), is increasingly applied in healthcare to improve efficiency and accuracy. Surgical reports are traditionally written post-procedure in free text, which is time-consuming and prone to errors. NLP-based approaches can transform unstructured data into structured, coherent reports, potentially enhancing documentation quality and workflow. This study explores the application of NLP to generate FESS surgical reports semi-automatically during procedures.

Data Highlights

Key Findings

• A neural language model with encoder-decoder architecture using bidirectional 2-layer LSTMs was successfully trained on retrospective FESS reports.
• 150 keywords related to surgical steps were defined and linked to corresponding sentences to enable keyword-based report generation.
• The model can generate coherent and semantically logical surgical report sentences from intraoperative keywords.
• Augmentation techniques improved model robustness despite imbalanced word occurrence in training data.
• The approach has potential to reduce the time and errors associated with manual report writing post-surgery.

Clinical Implications

Implementing this NLP-based semi-automated report generation tool during FESS procedures could streamline documentation, reduce physician workload, and minimize errors from memory-dependent report writing. This may allow surgeons to focus more on patient care and improve overall clinical workflow efficiency.

Conclusion

The study provides promising evidence that neural language models can semi-automatically produce detailed and coherent FESS surgical reports from intraoperative keywords, representing a valuable step toward AI-assisted surgical documentation.

References

1. Artificial Intelligence Market in Healthcare 2020 -- Market Value
2. AI Applications in Medical Imaging and Clinical Workflow Optimization
3. Natural Language Processing and Computer Vision in Healthcare
4. Previous Works on AI-Generated Radiology Reports and Surgical Navigation Prediction
5. SentencePiece Tokenizer for Language Model Training