Endoscopy 2019; 51(04): S80-S81
DOI: 10.1055/s-0039-1681406
ESGE Days 2019 oral presentations
Friday, April 5, 2019 17:00 – 18:30: IBD Club A
Georg Thieme Verlag KG Stuttgart · New York

AUTOMATED REAL TIME ENDOSCOPIC SCORING BASED ON MACHINE LEARNING IN ULCERATIVE COLITIS: RED DENSITY RELIABILITY AND RESPONSIVENESS STUDY

P Bossuyt
1   Department of Gastroenterology and Hepatology, University Hospitals Leuven, Leuven, Belgium
2   Department of Gastroenterology, Imelda General Hospital, Bonheiden, Belgium
,
S Vermeire
1   Department of Gastroenterology and Hepatology, University Hospitals Leuven, Leuven, Belgium
,
M Ferrante
1   Department of Gastroenterology and Hepatology, University Hospitals Leuven, Leuven, Belgium
,
T Makino
3   Pentax Medical, Tokyo, Japan
,
G De hertogh
4   Department of Pathology, University Hospitals Leuven, Leuven, Belgium
,
R Bisschops
1   Department of Gastroenterology and Hepatology, University Hospitals Leuven, Leuven, Belgium
› Author Affiliations
Further Information

Publication History

Publication Date:
18 March 2019 (online)

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Aims:

Histological remission predicts favourable long term outcome in ulcerative colitis (UC). Operator independent automated digital scoring of endoscopic and histological inflammation in UC could provide an objective and predictive evaluation tool of remission. The aim of this study was to test the operating properties of the Red Density (RD) score.

Methods:

Red Density uses machine learning to calculate a score based on automatic extraction of pixel data from endoscopic images. This algorithm incorporates colour data and vascular pattern recognition. In this prospective study, consecutive patients with UC with a flare were included. At baseline and 8 – 14 weeks after treatment escalation we recorded endoscopic (Red Density score, Ulcerative colitis endoscopic index of severity [UCEIS], Mayo endoscopic subscore [MES]), clinical (total Mayo, PRO-2) and histological data (Robarts histological index, Geboes score). Investigators were blinded for the RD score. Correlation was tested between RD and clinical, endoscopic and histological scores. Responsiveness was significant if standard effect size > 0.8.

Results:

Ten patients had 2 consecutive visits (M/F 4/6, median age 39y IQR 36 – 48). At baseline all patients had active endoscopic disease (median (IQR) UCEIS 4.5 (2.5 – 5); MES 2 (1.3 – 2). Nine patients had a change in their endoscopic score compared to baseline. The median delta in UCEIS and MES was 3 (IQR 2 – 4) (p = 0.009) and 1 (IQR 1 – 2) (p = 0.008) respectively. A significant number of patients achieved clinical, endoscopic and histological remission after treatment (all p < 0.03). Median RD score decreased significantly from baseline (166 to 58;p = 0.01). RD correlated moderate with clinical outcomes (r> 0.65, p = 0.001), and strong with both endoscopic (r> 0.75, p < 0.0001), and histological scores (r> 0.75, p < 0.0001). The standardized effect size for RD was 1.22.

Conclusions:

The automated digital endoscopic Red Density score demonstrates an excellent sensitivity to change after treatment escalation. Red Density is an ideal operator-independent digital tool for the evaluation of endoscopic and histological disease activity in UC.