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<p>Laryngopharyngeal reflux disease is a common yet diagnostically challenging condition in otorhinolaryngology due to heterogeneous symptoms and the subjective nature of endoscopic assessment. Although Reflux Symptom Index is widely used, interobserver variability limits diagnostic objectivity. Narrow band imaging (NBI), by enhancing mucosal vascular patterns, may provide objective markers of laryngeal inflammation. This study evaluates the role of NBI-based quantitative RGB image analysis in LPRD and its correlation with RSI severity. This observational study was conducted over 18&#xa0;months in 80 adults diagnosed with LPRD (RSI &gt; 10). Patients on proton pump inhibitors and those with confounding laryngeal or allergic conditions were excluded. All participants underwent NBI-assisted rigid video laryngoscopy. Images of the inter-arytenoid region were analyzed using ImageJ software, with mean red (R), green (G), and blue (B) channel values obtained from a standardized region of interest. Correlations between RSI scores and RGB parameters were assessed using Spearman’s rank correlation, and comparisons across RSI severity grades were performed using the Kruskal–Wallis test. The mean age of participants was 39.5&#xa0;years, with hoarseness, chronic cough, and globus sensation being the most common symptoms. A significant positive correlation was observed between RSI and the NBI-derived G channel (<i>ρ</i> = 0.310, <i>p</i> = 0.005) and composite RGB values (<i>p</i> = 0.017). Patients with severe RSI demonstrated significantly higher G-channel values compared to mild and moderate groups (<i>p</i> = 0.006), while R and B channels showed no significant association. Quantitative analysis of NBI images, particularly the green channel, correlates with symptom severity in LPRD and offers an objective adjunct to conventional assessment. This approach may reduce subjectivity and holds promise for future automated and artificial intelligence–based diagnostic applications.</p>

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Narrow Band Imaging Laryngoscopic Features of Laryngopharyngeal Reflux and Their Correlation with Reflux Symptom Index

  • 期刊论文

  • 发表日期：

    2026年3月13日

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  • Poonam Raj,
  • Revathi Shanmugam,
  • Kamalpreet Singh,
  • Meenakshi Rajput,
  • Uma Patnaik,
  • Amrindarjeet Kour

摘要

Laryngopharyngeal reflux disease is a common yet diagnostically challenging condition in otorhinolaryngology due to heterogeneous symptoms and the subjective nature of endoscopic assessment. Although Reflux Symptom Index is widely used, interobserver variability limits diagnostic objectivity. Narrow band imaging (NBI), by enhancing mucosal vascular patterns, may provide objective markers of laryngeal inflammation. This study evaluates the role of NBI-based quantitative RGB image analysis in LPRD and its correlation with RSI severity. This observational study was conducted over 18 months in 80 adults diagnosed with LPRD (RSI > 10). Patients on proton pump inhibitors and those with confounding laryngeal or allergic conditions were excluded. All participants underwent NBI-assisted rigid video laryngoscopy. Images of the inter-arytenoid region were analyzed using ImageJ software, with mean red (R), green (G), and blue (B) channel values obtained from a standardized region of interest. Correlations between RSI scores and RGB parameters were assessed using Spearman’s rank correlation, and comparisons across RSI severity grades were performed using the Kruskal–Wallis test. The mean age of participants was 39.5 years, with hoarseness, chronic cough, and globus sensation being the most common symptoms. A significant positive correlation was observed between RSI and the NBI-derived G channel (ρ = 0.310, p = 0.005) and composite RGB values (p = 0.017). Patients with severe RSI demonstrated significantly higher G-channel values compared to mild and moderate groups (p = 0.006), while R and B channels showed no significant association. Quantitative analysis of NBI images, particularly the green channel, correlates with symptom severity in LPRD and offers an objective adjunct to conventional assessment. This approach may reduce subjectivity and holds promise for future automated and artificial intelligence–based diagnostic applications.