Background/Aims: Neutrophil to lymphocyte ratio (NLR) and platelet to lymphocyte ratio (PLR) can serve as biomarkers for diagnosing and assessing disease activity in ulcerative colitis (UC). We investigated their clinical significance in UC.Methods: We analyzed 48 patients with UC who underwent measurement of fecal calprotectin (FC) and endoscopy and 96 age- and sex-matched healthy controls. NLR and PLR were compared between the patients and healthy controls. The endoscopic activity was divided into 2 groups: group 1 (mild to moderate inflammation) and group 2 (severe inflammation) according to the Mayo endoscopic subscore in UC.Results: To diagnose UC, the optimal cutoff of NLR and PLR was 2.26 (sensitivity 54.2%; specificity 90.6%; positive likelihood ratio 5.778, 95% confidence interval [CI] 2.944–11.339; area under the curve [AUC] 0.774, 95% CI, 0.690–0.859) and 179.8 (sensitivity 35.4%; specificity 90.6%; positive likelihood ratio 3.778, 95% CI 1.821–7.838; AUC 0.654, 95% CI 0.556–0.753), respectively. The optimal cutoff to differentiate group 1 and group 2 was 3.44, 175.9, and 453 µg/g for NLR, PLR, and FC, respectively (sensitivity, 63.6% vs. 90.9% vs. 81.8%; specificity, 81.1% vs. 78.4% vs. 73.0%; positive likelihood ratio, 3.364 vs. 4.205 vs. 3.027; AUC, 0.714 vs. 0.897 vs. 0.813). PLR had the highest AUC and positive likelihood ratio.Conclusions: NLR and PLR help differentiate patients with UC from healthy controls. NLR, PLR, and FC indicate endoscopic activity and may reflect intestinal mucosal conditions.

Artificial intelligence (AI), which has demonstrated outstanding achievements in image recognition, can be useful for the tedious capsule endoscopy (CE) reading. We aimed to develop a practical AI-based method that can identify various types of lesions and tried to evaluate the effectiveness of the method under clinical settings. A total of 203,244 CE images were collected from multiple centers selected considering the regional distribution. The AI based on the Inception-Resnet-V2 model was trained with images that were classified into two categories according to their clinical significance. The performance of AI was evaluated with a comparative test involving two groups of reviewers with different experiences. The AI summarized 67,008 (31.89%) images with a probability of more than 0.8 for containing lesions in 210,100 frames of 20 selected CE videos. Using the AI-assisted reading model, reviewers in both the groups exhibited increased lesion detection rates compared to those achieved using the conventional reading model (experts; 34.3%–73.0%; p = 0.029, trainees; 24.7%–53.1%; p = 0.029). The improved result for trainees was comparable to that for the experts (p = 0.057). Further, the AI-assisted reading model significantly shortened the reading time for trainees (1621.0–746.8 min; p = 0.029). Thus, we have developed an AI-assisted reading model that can detect various lesions and can successfully summarize CE images according to clinical significance. The assistance rendered by AI can increase the lesion detection rates of reviewers. Especially, trainees could improve their efficiency of reading as a result of reduced reading time using the AI-assisted model.

Three-dimensional (3D) reconstruction of capsule endoscopic images has been attempted for a long time to obtain more information on small bowel structures. Due to the limited hardware resources of capsule size and battery capacity, software approaches have been studied but have mainly exhibited inherent limitations. Recently, stereo camera-based capsule endoscopy, which can perform hardware-enabled 3D reconstruction, has been developed. We aimed to evaluate the feasibility of newly developed 3D capsule endoscopy in clinical practice. This study was a prospective, single-arm, feasibility study conducted at two university-affiliated hospitals in South Korea. Small bowel evaluation was performed using a newly developed 3D capsule endoscope for patients with obscure gastrointestinal bleeding, suspected or established Crohn’s disease, small bowel tumors, and abdominal pain of unknown origin. We assessed the technical limitations, performance, and safety of the new capsule endoscope. Thirty-one patients (20 men and 11 women; mean age: 44.5 years) were enrolled. There was no technical defect preventing adequate visualization of the small bowel. The overall completion rate was 77.4%, the detection rate was 64.5%, and there was no capsule retention. All capsule endoscopic procedures were completed uneventfully. In conclusion, newly developed 3D capsule endoscopy was safe and feasible, showing similar performance as conventional capsule endoscopy. Newly added features of 3D reconstruction and size measurement are expected to be useful in the characterization of subepithelial tumours.

Joon Seong Lee, Hyun-Sook Kim, Jung Rock Moon, Tom Ryu, Su Jin Hong, Young Sin Cho, Junseok Park, and Tae Hee Lee. J Neurogastroenterol Motil 2020;26:477-85. https://doi.org/10.5056/jnm19148

Shin Ok Jeong, Joon Seong Lee, Tae Hee Lee, Su Jin Hong, Young Sin Cho, Junseok Park, Seong Ran Jeon, Hyun Gun Kim, and Jin-Oh Kim. J Neurogastroenterol Motil 2021;27:231-9. https://doi.org/10.5056/jnm20114

Jae Myung Cha, Myoungsuk Kim, Hyeong Ho Jo, Won-Woo Seo, Sang Youl Rhee, Ji Hyun Kim, Gwang Ha Kim, Junseok Park. Gut and Liver 2024;18:814-23. https://doi.org/10.5009/gnl230406