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Updated: May 2, 2026

Integrating Augmented Reality Tools in Breast Cancer Related Lymphedema Prognostication and Diagnosis

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Published on: February 6, 2020

自然言語処理と深層学習を用いた自家乳房再建における患者の感情分析

Melanie J Wang, Arman J Fijany, Cole A Holan

Aesthetic surgery journal. Open forum

|December 26, 2025

要旨

Background:

Use of autologous flaps for postmastectomy reconstruction has increased. Public forums offer large-scale patient narratives that can be analyzed with natural language processing (NLP) models, such as RoBERTa.

Objectives:

This study aims to assess patient-reported sentiment and emotions toward autologous reconstruction with deep inferior epigastric perforator (DIEP), transverse rectus abdominis myocutameous (TRAM), and latissimus dorsi (LD) flaps.

Methods:

Public reviews from RealSelf (Seattle, WA) referencing DIEP, TRAM, or LD flaps were collected. Two NLP models were applied: (1) a binary sentiment classifier (positive/negative) and (2) an emotion model scoring fear, sadness, anger, disgust, neutral, surprise, and joy; each review was labeled by its highest-scoring emotion.

Results:

Two hundred and twelve posts were analyzed: 153 DIEP, 20 TRAM, and 39 LD. DIEP had the highest mean positive sentiment (0.627); TRAM had the lowest (0.517). In the emotion model, "joy" was most common for DIEP (68/153; mean 0.389) and TRAM (7/20; mean 0.299). LD posts were most often "neutral" (9/39; mean 0.267).

Conclusions:

NLP of public patient reviews suggests DIEP flaps are the most discussed and are associated with more positive sentiment than TRAM or LD reconstructions. Emotion profiles indicate generally joyful narratives for DIEP, neutral for LD, and comparatively less positive for TRAM. This approach complements traditional outcomes research by capturing real-world patient experience at scale.

キーワード:

自家乳房再建感情分析自然言語処理深層学習 DIEP皮弁 TRAM皮弁 LD皮弁患者経験

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