{
  "abstract": "Background Ordinal Mayo Endoscopic Scoring collapses mucosal inflammation into four discrete categories, obscuring within-grade heterogeneity and limiting clinical trial sensitivity. Patients assigned identical Mayo grades may have meaningfully different inflammatory burdens, reducing statistical power to detect treatment response. We developed and validated a continuous AI-derived inflammation index (0 -10) from endoscopic images to address this fundamental limitation.Methods An EfficientNet-B3 regression model was trained on 9,590 colonoscopy images from the LIMUC dataset (564 UC patients) to output a continuous inflammation index (0 -10). Performance was assessed against expert Mayo grades using Pearson correlation, Spearman rank correlation, and mean absolute error (MAE) on a held-out test set (n=1,686). Within-grade heterogeneity was quantified by intra-class standard deviation. Trial sensitivity was evaluated by simulating response detection (n=200), comparing ordinal versus continuous scoring using independent samples t-tests.Results Training converged stably with the best validation MAE=0.297 ( IDDF2026-ABS-0257 Figure 1. Regression training curve for the continuous inflammation index). The continuous index achieved MAE=0.297, Pearson r=0.880, and Spearman ρ=0.813 against expert ordinal grades (IDDF2026-ABS-0257 Figure 4. Continuous inflammation index vs ordinal Mayo grade). Mean index values mapped logically across severity: Mayo 0: 0.72±1.03, Mayo 1: 3.11±1.56, Mayo 2: 6.50±1.84, Mayo 3: 8.60±1.33, with continuous index distributions showing progressive separation across all four Mayo grades (IDDF2026-ABS-0257 Figure 5. Continuous inflammation index distribution by Mayo endoscopic grade).Critically, within-grade standard deviations of 1.03-1.84 demonstrate substantial inflammatory heterogeneity invisible to ordinal scoring, with Mayo 2 showing the greatest variation (σ=1.84) (IDDF2026-ABS-0257 Figure 3. Within-grade heterogeneity of the continuous index per Mayo grade), confirming that patients with identical Mayo grades harbour ­meaningfully different inflammatory burdens. Trial simulation confirmed both scoring systems significantly separated responders from non-responders; however, the continuous index provided superior distributional separation (p=4.94×10–97 vs p=2.81×10–112) (IDDF2026-ABS-0257 Figure 2. Trial sensitivity simulation), supporting smaller required sample sizes and enhanced sensitivity for treatment response detection.Conclusions An AI-derived continuous inflammation index captures mucosal heterogeneity invisible to ordinal Mayo scoring, with strong correlation to expert grades (r=0.880) and sub-grade resolution (MAE=0.297). The substantial within-grade variance, particularly at Mayo 2 (σ=1.84), supports the adoption of continuous endpoints in UC clinical trials to improve sensitivity, reduce sample size requirements, and accelerate detection of treatment response.Abstract IDDF2026-ABS-0257 Figure 1Abstract IDDF2026-ABS-0257 Figure 2Abstract IDDF2026-ABS-0257 Figure 3Abstract IDDF2026-ABS-0257 Figure 4Abstract IDDF2026-ABS-0257 Figure 5",
  "authors": [
    {
      "affiliations": [
        "Rutgers University, United States"
      ],
      "name": "Lawin Steve Mathew Lasington"
    },
    {
      "affiliations": [
        "New York Medical College, United States"
      ],
      "name": "Jeril Lasington"
    },
    {
      "affiliations": [
        "Boston University, United States"
      ],
      "name": "Swamynathan Umamaheshwaran"
    }
  ],
  "title": "IDDF2026-ABS-0257 A continuous AI-derived inflammation index for sensitive monitoring of UC treatment response in clinical trials",
  "uid": "bddaaf99-b6a3-5f14-99fb-84fe09a77f11"
}