Current Status of Colorectal Cancer and Its Public Health Burden in Thailand

Kasenee Tiankanon,Satimai Aniwan,Rungsun Rerknimitr 대한소화기내시경학회 2021 Clinical Endoscopy Vol.54 No.4

Colorectal cancer (CRC) accounts for approximately 10.3% of new cancer cases in Thailand and is currently the 3rd most prevalentcancer found among the Thai population. Starting in 2017, the Thai government announced the national CRC screening program asa response to this important issue. Among the 70 million people currently residing in Thailand, 14 million require screening, whilethere are approximately a total of 1,000 endoscopists available to perform colonoscopy. Due to the limited resources and shortage ofendoscopists in Thailand, applying a population-based one-step colonoscopy program as a primary screening method is not feasible. To reduce colonoscopy workload, with the help of others, including village health volunteers, institution-based health personnel,reimbursement coders, pathologists, and patients due for CRC screening, a two-step approach of one-time fecal immunochemicaltest (FIT), which prioritizes and filters out subjects for colonoscopy, is chosen. Moreover, additional adjustments to the optimal FITcutoff value and the modified Asia-Pacific Colorectal Screening risk score, including body weight, were proposed to stratify thepriority of colonoscopy schedule. This article aims to give an overview of the past and current policy developmental strategies andthe current status of the Thailand CRC screening program.

Performance comparison between two computer-aided detection colonoscopy models by trainees using different false positive thresholds: a cross-sectional study in Thailand

Kasenee Tiankanon,Julalak Karuehardsuwan,Satimai Aniwan,Parit Mekaroonkamol,Panukorn Sunthornwechapong,Huttakan Navadurong,Kittithat Tantitanawat,Krittaya Mekritthikrai,Salin Samutrangsi,Peerapon Vate 대한소화기내시경학회 2024 Clinical Endoscopy Vol.57 No.2

Background/Aims: This study aims to compare polyp detection performance of “Deep-GI,” a newly developed artificial intelligence (AI) model, to a previously validated AI model computer-aided polyp detection (CADe) using various false positive (FP) thresholds and determining the best threshold for each model. Methods: Colonoscopy videos were collected prospectively and reviewed by three expert endoscopists (gold standard), trainees, CADe (CAD EYE; Fujifilm Corp.), and Deep-GI. Polyp detection sensitivity (PDS), polyp miss rates (PMR), and false-positive alarm rates (FPR) were compared among the three groups using different FP thresholds for the duration of bounding boxes appearing on the screen. Results: In total, 170 colonoscopy videos were used in this study. Deep-GI showed the highest PDS (99.4% vs. 85.4% vs. 66.7%, p<0.01) and the lowest PMR (0.6% vs. 14.6% vs. 33.3%, p<0.01) when compared to CADe and trainees, respectively. Compared to CADe, Deep-GI demonstrated lower FPR at FP thresholds of ≥0.5 (12.1 vs. 22.4) and ≥1 second (4.4 vs. 6.8) (both p<0.05). However, when the threshold was raised to ≥1.5 seconds, the FPR became comparable (2 vs. 2.4, p=0.3), while the PMR increased from 2% to 10%. Conclusions: Compared to CADe, Deep-GI demonstrated a higher PDS with significantly lower FPR at ≥0.5- and ≥1-second thresholds. At the ≥1.5-second threshold, both systems showed comparable FPR with increased PMR.

Perception of Gastrointestinal Endoscopy Personnel on Society Recommendations on Personal Protective Equipment, Case Selection, and Scope Cleaning During Covid-19 Pandemic: An International Survey Study

Parit Mekaroonkamol,Kasenee Tiankanon,Rapat Pittayanon,Wiriyaporn Ridtitid,Fariha Shams,Ghias Un Nabi Tayyab,Julia Massaad,Saurabh Chawla,Stanley Khoo,Siriboon Attasaranya,Nonthalee Pausawasdi,Qiang C 대한소화기내시경학회 2022 Clinical Endoscopy Vol.55 No.2

Background/Aims: The Thai Association for Gastrointestinal Endoscopy published recommendations on safe endoscopyduring the coronavirus disease 2019 (COVID-19) pandemic. This study aimed to assess the practicality and applicability of therecommendations and the perceptions of endoscopy personnel on them. Methods: A validated questionnaire was sent to 1290 endoscopy personnel globally. Of these, the data of all 330 responders (25.6%)from 15 countries, related to the current recommendations on proper personal protective equipment (PPE), case selection, scopecleaning, and safety perception, were analyzed. Ordinal logistic regression was used to determine the relationships between thevariables. Results: Despite an overwhelming agreement with the recommendations on PPE (94.5%) and case selection (95.5%), theirpracticality and applicability on PPE recommendations and case selection were significantly lower (p=0.001, p=0.047, p<0.001, andp=0.032, respectively). Factors that were associated with lower sense of safety in endoscopy units were younger age (p=0.004), lessworking experience (p=0.008), in-training status (p=0.04), and higher national prevalence of COVID-19 (p=0.003). High prevalentcountries also had more difficulty implementing the guidelines (p<0.001) and they considered the PPE recommendations lesspractical and showed lower agreement with them (p<0.001 and p=0.008, respectively). A higher number of in-hospital COVID-19patients was associated with less agreement with PPE recommendations (p=0.039). Conclusions: Using appropriate PPE and case selection in endoscopic practice during a pandemic remains a challenge. Resourceavailability and local prevalence are critical factors influencing the adoption of the current guidelines.

A New Paradigm Shift in Gastroparesis Management

( Parit Mekaroonkamol ),( Kasenee Tiankanon ),( Rungsun Rerknimitr ) 대한소화기학회 2022 Gut and Liver Vol.16 No.6

Gastroparesis, once regarded as a rare disease, is difficult to diagnose and challenging to treat; there were many breakthrough advances in the 2010s, shifting the paradigm of the understanding of this complex entity and its management. Similar to diabetes, its increasing prevalence reflects increased accessibility to diagnostic modalities and suggests that gastroparesis was underacknowledged in the past. Major developments in the three main aspects of the disease include the discovery of smooth muscle cells, interstitial cells of Cajal, PDGFRα+ cells syncytium, rather than interstitial cells of Cajal alone, as the main gastric pacemaker unit; the development of validated point-of-care diagnostic modalities such as a wireless motility capsule, the carbon 13-labeled breath test, and impedance planimetry; and the introduction of novel minimally invasive therapeutic options such as newer pharmacologic agents and gastric peroral endoscopic pyloromyotomy. All aspects of these advances will be discussed further in this review. (Gut Liver 2022;16:825-839)

Real-time semantic segmentation of gastric intestinal metaplasia using a deep learning approach

Vitchaya Siripoppohn,Rapat Pittayanon,Kasenee Tiankanon,Natee Faknak,Anapat Sanpavat,Naruemon Klaikaew,Peerapon Vateekul,Rungsun Rerknimitr 대한소화기내시경학회 2022 Clinical Endoscopy Vol.55 No.3

Background/Aims: Previous artificial intelligence (AI) models attempting to segment gastric intestinal metaplasia (GIM) areas havefailed to be deployed in real-time endoscopy due to their slow inference speeds. Here, we propose a new GIM segmentation AI modelwith inference speeds faster than 25 frames per second that maintains a high level of accuracy. Methods: Investigators from Chulalongkorn University obtained 802 histological-proven GIM images for AI model training. Fourstrategies were proposed to improve the model accuracy. First, transfer learning was employed to the public colon datasets. Second, animage preprocessing technique contrast-limited adaptive histogram equalization was employed to produce clearer GIM areas. Third,data augmentation was applied for a more robust model. Lastly, the bilateral segmentation network model was applied to segment GIMareas in real time. The results were analyzed using different validity values. Results: From the internal test, our AI model achieved an inference speed of 31.53 frames per second. GIM detection showed sensitivity,specificity, positive predictive, negative predictive, accuracy, and mean intersection over union in GIM segmentation values of 93%,80%, 82%, 92%, 87%, and 57%, respectively. Conclusions: The bilateral segmentation network combined with transfer learning, contrast-limited adaptive histogram equalization,and data augmentation can provide high sensitivity and good accuracy for GIM detection and segmentation.