Hannah Lee

Esophageal cancer is associated with a relatively poor prognosis. Early detection and diagnosis are important for the survival and prognosis of patients with esophageal cancer. Recently, the development of artificial intelligence (AI) and its application in clinical medicine has led to remarkable progress in various endoscopic fields, including the detection of Barrett’s esophagus and esophageal cancer. Compared to human errors induced by fatigue and impairment in diagnostic precision, progression in the field of AI, including deep learning and convolutional neural networks, has resulted in improved diagnostic accuracy. Subtle microvascular changes in lesions, visual disturbances, and fatigue, as well as varying endoscopic expertise depending on the operator, are the major causes of missing rates in detecting cancerous lesions. Specifically, the rate of missed diagnoses of esophageal squamous cell carcinoma reaches 4%–17%. In contrast, deep learning-based AI assists in the diagnosis of esophageal squamous cell carcinoma, evaluation of invasion depth, microvascular classification, and delineation of the margin of the lesion. In this review article, we investigated the development of the current AI model introduced for esophageal precancerous lesions, including Barrett’s and esophageal cancer.

Peptic ulcer bleeding (PUB) is the most common cause of non-variceal upper gastrointestinal bleeding, although its incidence is decreasing worldwide. Current epidemiological evidence demonstrates the positive influence of Helicobacter pylori eradication on outcomes, owing to the widespread introduction of proton pump inhibitor (PPI) treatment. However, PUB is still commonly encountered in clinical settings given the increasing indications for nonsteroidal anti-inflammatory drugs, anticoagulants, and antiplatelet agents in the aging population. Regardless of advancements made in the endoscopic approach and pharmacological treatment, upper gastrointestinal bleeding has an inevitable mortality rate ranging from 7% to 11%. Endoscopic treatment is recommended for peptic ulcers with current bleeding, vessel exposure, or clotting. However, the development of effective high-dose PPIs has resulted in a decrease in rebleeding events after endoscopic treatment. Pharmacological management consisting of post-endoscopic treatment was more effective than pre-endoscopic treatment. As PPIs ensure that gastric acidity remains above pH 6, they are essential treatments for PUB. This review aimed to investigate the current strategies for PUB pharmacological management.

Expanding role of artificial intelligence in gastric neoplasms: moving beyond resource limitations

Hannah Lee, Jun-Won Chung, Kyoung Oh Kim, Kwang An Kwon, Jung Ho Kim. Gut and Liver -0001;0:. https://doi.org/10.5009/gnl250211

Background/Objectives: Controlling colonoscopic quality is important in the detection of colon polyps during colonoscopy as it reduces the overall long-term colorectal cancer risk. Artificial intelligence has recently been introduced in various medical fields. In this study, we aimed to validate a previously developed artificial intelligence (AI) computer-aided detection (CADe) algorithm called ALPHAON^® and compare outcomes with previous studies that showed that AI outperformed and assisted endoscopists of diverse levels of expertise in detecting colon polyps. Methods: We used the retrospective data of 500 still images, including 100 polyp images and 400 healthy colon images. In addition, we validated the CADe algorithm and compared its diagnostic performance with that of two expert endoscopists and six trainees from Gachon University Gil Medical Center. After a washing-out period of over 2 weeks, endoscopists performed polyp detection on the same dataset with the assistance of ALPHAON^®. Results: The CADe algorithm presented a high capability in detecting colon polyps, with an accuracy of 0.97 (95% CI: 0.96 to 0.99), sensitivity of 0.91 (95% CI: 0.85 to 0.97), specificity of 0.99 (95% CI: 0.97 to 0.99), and AUC of 0.967. When evaluating and comparing the polyp detection ability of ALPHAON^® with that of endoscopists with different levels of expertise (regarding years of endoscopic experience), it was found that ALPHAON^® outperformed the experts in accuracy (0.97, 95% CI: 0.96 to 0.99), sensitivity (0.91, 95% CI: 0.85 to 0.97), and specificity (0.99, 95% CI: 0.97 to 0.99). After a washing-out period of over 2 weeks, the overall capability significantly improved for both experts and trainees with the assistance of ALPHAON^®. Conclusions: The high performance of the CADe algorithm system in colon polyp detection during colonoscopy was verified. The sensitivity of ALPHAON^® led to it outperforming the experts, and it demonstrated the ability to enhance the polyp detection ability of both experts and trainees, which suggests a significant possibility of ALPHAON^® being able to provide endoscopic assistance.

Background/Objectives: Gastric cancer ranks fifth for incidence and fourth in the leading causes of mortality worldwide. In this study, we aimed to validate previously developed artificial intelligence (AI) computer-aided detection (CADe) algorithm, called ALPHAON^® in detecting gastric neoplasm. Methods: We used the retrospective data of 500 still images, including 5 benign gastric ulcers, 95 with gastric cancer, and 400 normal images. Thereby we validated the CADe algorithm measuring accuracy, sensitivity, and specificity with the result of receiver operating characteristic curves (ROC) and area under curve (AUC) in addition to comparing the diagnostic performance status of four expert endoscopists, four trainees, and four beginners from two university-affiliated hospitals with CADe algorithm. After a washing-out period of over 2 weeks, endoscopists performed gastric detection on the same dataset of the 500 endoscopic images again marked by ALPHAON^®. Results: The CADe algorithm presented high validity in detecting gastric neoplasm with accuracy (0.88, 95% CI: 0.85 to 0.91), sensitivity (0.93, 95% CI: 0.88 to 0.98), specificity (0.87, 95% CI: 0.84 to 0.90), and AUC (0.962). After a washing-out period of over 2 weeks, overall validity improved in the trainee and beginner groups with the assistance of ALPHAON^®. Significant improvement was present, especially in the beginner group (accuracy 0.94 (0.93 to 0.96) p < 0.001, sensitivity 0.87 (0.82 to 0.92) p < 0.001, specificity 0.96 (0.95 to 0.97) p < 0.001). Conclusions: The high validation performance state of the CADe algorithm system was verified. Also, ALPHAON^® has demonstrated its potential to serve as an endoscopic educator for beginners improving and making progress in sensitivity and specificity.

Background/Aims: Rapid and accurate diagnostic tools are essential for the timely recognition of Helicobacter pylori (H. pylori) in clinical practice. The rapid urease test (RUT) is a comparatively accurate and time-saving method recommended as a first-line diagnostic test. The primary objective of conducting the RUT is to obtain rapid results, thus enabling the initiation of an eradication therapy based on clarithromycin resistance testing. This study aimed to assess the reaction time and accuracy of a new liquid-type RUT. Method: In this prospective study, consecutive dyspeptic or check-up patients referred to our clinic for endoscopy were assessed to evaluate the rapidity and accuracy of a novel liquid-type RUT (Helicotest^®, WON Medical, Bucheon, Republic of Korea) compared with another commercial RUT kit (HP kit, Chong Kun Dang, Seoul, Republic of Korea) and a real-time quantitative PCR-based assay (Seeplex^® H.pylori-ClaR Detection, Seegene, Republic of Korea). RUTs were analyzed at 10 min, 30 min, 60 min, and 120 min. Results: Among the 177 enrolled patients, 38.6% were infected with H. pylori. The positivity rates of the liquid-type RUT were 26.1, 35.8, 39.2%, and 41.5% at 10, 30, 60, and 120 min, respectively. When compared with the HP kit test, the time needed to confirm positivity was significantly reduced by 28.6 min (95% CI, 16.60–39.73, p < 0.0001). Helicotest^® had a greater accuracy (96.02 ± 1.47), sensitivity (98.53 ± 1.46) and NPV (99.03 ± 0.97) compared to the HP kit. Conclusions: Compared to the commonly used RUT, the new liquid-type RUT presented faster and reliable results. Such findings could improve H. pylori treatment outcomes, particularly in outpatient clinical settings.

Lee J, et al. J Gastric Cancer. 2023 Jul;23(3):375-387. https://doi.org/10.5230/jgc.2023.23.e31