Endoscopic submucosal dissection for early esophageal squamous cell carcinoma after ligation and sclerotherapy of esophageal and gastric varices.

Esophageal cancer ranked ten of the most common cancers in China. With the advancement of high-quality endoscopy and chromoendoscopic technique, early esophageal cancer can be diagnosed more easily, even combined with esophageal-gastric fundal varices. Endoscopic resection of early esophageal cancer is a minimally invasive treatment method for early esophageal cancer, and endoscopic submucosal dissection (ESD) is one of the standard treatments for early esophageal cancer in view of the risk of bleeding, the patient in this study successfully received ESD treatment after using endoscopic variceal ligation and endoscopic injection of tissue glue and sclerosing agent before ESD surgery. ESD treatment is safe and feasible for early esophageal cancer patients with cirrhosis of esophageal-gastric fundal varices.

Transformer-based multi-task learning for classification and segmentation of gastrointestinal tract endoscopic images.

Despite being widely utilized to help endoscopists identify gastrointestinal (GI) tract diseases using classification and segmentation, models based on convolutional neural network (CNN) have difficulties in distinguishing the similarities among some ambiguous types of lesions presented in endoscopic images, and in the training when lacking labeled datasets. Those will prevent CNN from further improving the accuracy of diagnosis. To address these challenges, we first proposed a Multi-task Network (TransMT-Net) capable of simultaneously learning two tasks (classification and segmentation), which has the transformer designed to learn global features and can combine the advantages of CNN in learning local features so that to achieve a more accurate prediction in identifying the lesion types and regions in GI tract endoscopic images. We further adopted the active learning in TransMT-Net to tackle the labeled image-hungry problem. A dataset was created from the CVC-ClinicDB dataset, Macau Kiang Wu Hospital, and Zhongshan Hospital to evaluate the model performance. Then, the experimental results show that our model not only achieved 96.94% accuracy in the classification task and 77.76% Dice Similarity Coefficient in the segmentation task but also outperformed those of other models on our test set. Meanwhile, active learning also produced positive results for the performance of our model with a small-scale initial training set, and even its performance with 30% of the initial training set was comparable to that of most comparable models with the full training set. Consequently, the proposed TransMT-Net has demonstrated its potential performance in GI tract endoscopic images and it through active learning can alleviate the shortage of labeled images.

CLELNet: A continual learning network for esophageal lesion analysis on endoscopic images.

BACKGROUND AND OBJECTIVE: A deep learning-based intelligent diagnosis system can significantly reduce the burden of endoscopists in the daily analysis of esophageal lesions. Considering the need to add new tasks in the diagnosis system, a deep learning model that can train a series of tasks incrementally using endoscopic images is essential for identifying the types and regions of esophageal lesions. METHOD: In this paper, we proposed a continual learning-based esophageal lesion network (CLELNet), in which a convolutional autoencoder was designed to extract representation features of endoscopic images among different esophageal lesions. The proposed CLELNet consists of shared layers and task-specific layers. Shared layers are used to extract common features among different lesions while task-specific layers can complete different tasks. The first two tasks trained by the CLELNet are the classification (task 1) and the segmentation (task 2). We collected a dataset of esophageal endoscopic images from Macau Kiang Wu Hospital for training and testing the CLELNet. RESULTS: The experimental results showed that the classification accuracy of task 1 was 95.96%, and the Intersection Over Union and the Dice Similarity Coefficient of task 2 were 65.66% and 78.08%, respectively. CONCLUSIONS: The proposed CLELNet can realize task-incremental learning without forgetting the previous tasks and thus become a useful computer-aided diagnosis system in esophageal lesions analysis.

Deep learning based radiomics for gastrointestinal cancer diagnosis and treatment: A minireview.

Gastrointestinal (GI) cancers are the major cause of cancer-related mortality globally. Medical imaging is an important auxiliary means for the diagnosis, assessment and prognostic prediction of GI cancers. Radiomics is an emerging and effective technology to decipher the encoded information within medical images, and traditional machine learning is the most commonly used tool. Recent advances in deep learning technology have further promoted the development of radiomics. In the field of GI cancer, although there are several surveys on radiomics, there is no specific review on the application of deep-learning-based radiomics (DLR). In this review, a search was conducted on Web of Science, PubMed, and Google Scholar with an emphasis on the application of DLR for GI cancers, including esophageal, gastric, liver, pancreatic, and colorectal cancers. Besides, the challenges and recommendations based on the findings of the review are comprehensively analyzed to advance DLR.

Diagnosis of Esophageal Lesions by Multi-Classification and Segmentation Using an Improved Multi-Task Deep Learning Model.

It is challenging for endoscopists to accurately detect esophageal lesions during gastrointestinal endoscopic screening due to visual similarities among different lesions in terms of shape, size, and texture among patients. Additionally, endoscopists are busy fighting esophageal lesions every day, hence the need to develop a computer-aided diagnostic tool to classify and segment the lesions at endoscopic images to reduce their burden. Therefore, we propose a multi-task classification and segmentation (MTCS) model, including the Esophageal Lesions Classification Network (ELCNet) and Esophageal Lesions Segmentation Network (ELSNet). The ELCNet was used to classify types of esophageal lesions, and the ELSNet was used to identify lesion regions. We created a dataset by collecting 805 esophageal images from 255 patients and 198 images from 64 patients to train and evaluate the MTCS model. Compared with other methods, the proposed not only achieved a high accuracy (93.43%) in classification but achieved a dice similarity coefficient (77.84%) in segmentation. In conclusion, the MTCS model can boost the performance of endoscopists in the detection of esophageal lesions as it can accurately multi-classify and segment the lesions and is a potential assistant for endoscopists to reduce the risk of oversight.

Hepatoprotective effect of Qushihuayu formula on non-alcoholic steatohepatitis induced by MCD diet in rat.

BACKGROUND: Non-alcoholic steatohepatitis (NASH) is an advanced form of non-alcoholic fatty liver disease (NAFLD) for which there is yet any standard pharmacotherapy. Traditional Chinese medicine formula such as Qushihuayu (QSHY) composing of multiple bioactive compounds has been used to treat NAFLD and NASH and shows beneficial effects over single compound treatment. This study aimed to investigate the mechanism of hepatoprotective effect of QSHY formula using a rat model. METHODS: Six-weeks old male Wistar rats were given methionine/choline supplemented (MCS) diet for 8 weeks and used as the blank control. Another 7 rats, which received methionine/choline deficient (MCD) diet in the first 6 weeks and a MCS&MCD (1:1) mixture diet in the last 2 weeks, were used as the model group. The groups of QSHY pre-treatment, low dosage, medium dosage and high dosage were given the same diet as the model group. Except for pre-treatment group (1 week in advanced of other groups), all QSHY treatment groups received QSHY formula by gavage every day since the MCD diet started. RESULTS: In the MCD diet group, the QSHY formula decreased the serum ALT and AST levels, lipid droplets, inflammation foci, FAS and α-SMA protein expression than MCD diet group. MAPK pathways phospharylation were markedly depressed by the QSHY formula. Moreover, QSHY formula enhanced PPAR-γ and p-p65 translocating into nucleus. The administration of QSHY increased hepatic mRNA levels of Transcription Factor 1 alpha (HNF1A), Hepatocyte Nuclear Factor 4 alpha (HNF4A) and Forkhead box protein A3 (FOXA3) which play a pivotal role in Hepatic stellate cell (HSCs) reprogramming. CONCLUSION: These findings suggest that QSHY formula exerts a hepatoprotective effect against steatosis and fibrosis presumably via depressed MAPK pathways phosphorylation, reinforcement of PPAR-γ and p-p65 translocating into nucleus and enhanced HSCs reprogramming.

Comparative study on artificial intelligence systems for detecting early esophageal squamous cell carcinoma between narrow-band and white-light imaging.

BACKGROUND: Non-magnifying endoscopy with narrow-band imaging (NM-NBI) has been frequently used in routine screening of esophagus squamous cell carcinoma (ESCC). The performance of NBI for screening of early ESCC is, however, significantly affected by operator experience. Artificial intelligence may be a unique approach to compensate for the lack of operator experience. AIM: To construct a computer-aided detection (CAD) system for application in NM-NBI to identify early ESCC and to compare it with our previously reported CAD system with endoscopic white-light imaging (WLI). METHODS: A total of 2167 abnormal NM-NBI images of early ESCC and 2568 normal images were collected from three institutions (Zhongshan Hospital of Fudan University, Xuhui Hospital, and Kiang Wu Hospital) as the training dataset, and 316 pairs of images, each pair including images obtained by WLI and NBI (same part), were collected for validation. Twenty endoscopists participated in this study to review the validation images with or without the assistance of the CAD systems. The diagnostic results of the two CAD systems and improvement in diagnostic efficacy of endoscopists were compared in terms of sensitivity, specificity, accuracy, positive predictive value, and negative predictive value. RESULTS: The area under receiver operating characteristic curve for CAD-NBI was 0.9761. For the validation dataset, the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of CAD-NBI were 91.0%, 96.7%, 94.3%, 95.3%, and 93.6%, respectively, while those of CAD-WLI were 98.5%, 83.1%, 89.5%, 80.8%, and 98.7%, respectively. CAD-NBI showed superior accuracy and specificity than CAD-WLI (P = 0.028 and P ≤ 0.001, respectively), while CAD-WLI had higher sensitivity than CAD-NBI (P = 0.006). By using both CAD-WLI and CAD-NBI, the endoscopists could improve their diagnostic efficacy to the highest level, with accuracy, sensitivity, and specificity of 94.9%, 92.4%, and 96.7%, respectively. CONCLUSION: The CAD-NBI system for screening early ESCC has higher accuracy and specificity than CAD-WLI. Endoscopists can achieve the best diagnostic efficacy using both CAD-WLI and CAD-NBI.

Multi-Task Model for Esophageal Lesion Analysis Using Endoscopic Images: Classification with Image Retrieval and Segmentation with Attention.

The automatic analysis of endoscopic images to assist endoscopists in accurately identifying the types and locations of esophageal lesions remains a challenge. In this paper, we propose a novel multi-task deep learning model for automatic diagnosis, which does not simply replace the role of endoscopists in decision making, because endoscopists are expected to correct the false results predicted by the diagnosis system if more supporting information is provided. In order to help endoscopists improve the diagnosis accuracy in identifying the types of lesions, an image retrieval module is added in the classification task to provide an additional confidence level of the predicted types of esophageal lesions. In addition, a mutual attention module is added in the segmentation task to improve its performance in determining the locations of esophageal lesions. The proposed model is evaluated and compared with other deep learning models using a dataset of 1003 endoscopic images, including 290 esophageal cancer, 473 esophagitis, and 240 normal. The experimental results show the promising performance of our model with a high accuracy of 96.76% for the classification and a Dice coefficient of 82.47% for the segmentation. Consequently, the proposed multi-task deep learning model can be an effective tool to help endoscopists in judging esophageal lesions.

Intelligent diagnosis of gastric intestinal metaplasia based on convolutional neural network and limited number of endoscopic images.

BACKGROUND: Gastric intestinal metaplasia (GIM) is a precancerous lesion of gastric cancer. Currently, diagnosis of GIM is based on the experience of a physician, which is liable to interobserver variability. Thus, an intelligent diagnostic (ID) system, based on narrow-band and magnifying narrow-band images, was constructed to provide objective assistance in the diagnosis of GIM. METHOD: We retrospectively collected 1880 endoscopic images (1048 GIM and 832 non-GIM) via biopsy from 336 patients confirmed histologically as GIM or non-GIM, from the Kiang Wu Hospital, Macau. We developed an ID system with these images using a modified convolutional neural network algorithm. A separate test dataset containing 477 pathologically confirmed images (242 GIM and 235 non-GIM) from 80 patients was used to test the performance of the ID system. Experienced endoscopists also examined the same test dataset, for comparison with the ID system. One of the challenges faced in this study was that it was difficult to obtain a large number of training images. Thus, data augmentation and transfer learning were applied together. RESULTS: The area under the receiver operating characteristic curve was 0.928 for the pre-patient analysis of the ID system, while the sensitivities, specificities, and accuracies of the ID system against those of the human experts were (91.9% vs. 86.5%, p-value = 1.000) (86.0% vs. 81.4%, p-value = 0.754), and (88.8% vs. 83.8%, p-value = 0.424), respectively. Even though the three indices of the ID system were slightly higher than those of the human experts, there were no significant differences. CONCLUSIONS: In this pilot study, a novel ID system was developed to diagnose GIM. This system exhibits promising diagnostic performance. It is believed that the proposed system has the potential for clinical application in the future.

Hepatoprotective Effect of Jianpi Huoxue Formula on Nonalcoholic Fatty Liver Disease Induced by Methionine-Choline-Deficient Diet in Rat.

In parallel with the prevalence metabolic syndrome, nonalcoholic fatty liver disease (NAFLD) has become the most common chronic liver disease in most countries. It features a constellation of simple steatosis, nonalcoholic steatohepatitis (NASH), fibrosis, cirrhosis, and even hepatocellular carcinoma. There are no approved drugs for effective management of NAFLD and NASH. Jianpi Huoxue formula (JPHX) mainly consists of Atractylodes macrocephal (Baizhu), Salvia miltiorrhiza (Danshen), Rasux Paeonia Alba (Baishao), Rhizoma Alismatis (Zexie), and Fructus Schisandrae Chinensis (Wuweizi), which may have beneficial effects on NAFLD. The aim of the study was to identify the effect of JPHX on NAFLD. A NAFLD model was induced by methionine-choline-deficient food (MCD) in Wistar rats and orally administered with simultaneous JPHX, once a day for 8 weeks. Hepatocellular injury, lipid profile, inflammation, fibrosis, and apoptosis were evaluated. The results showed that JPHX significantly decreased the abnormal serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels compared with the MCD model (P<0.05). Furthermore, JPHX protected MCD diet-fed rats from accumulation of hepatic triglycerides (TG) and total cholesterol (TC). Histological examination demonstrated that JPHX noticeably normalized the NAFLD activity score (NAS). Moreover, JPHX ameliorated liver inflammation by decreasing TNF-α levels and reduced collagen and matrix metalloproteinases in MCD diet-fed rats. In addition, JPHX prevented rats from MCD-induced cellular apoptosis, as suggested by TUNEL staining, and suppressed the activation of caspase 3 and 7 proteins. JPHX also inhibited the phosphorylation of JNK. In conclusion, JPHX exhibited a hepatoprotective effect against NAFLD in an MCD experimental model.

Using a deep learning system in endoscopy for screening of early esophageal squamous cell carcinoma (with video).

BACKGROUND AND AIMS: Few artificial intelligence-based technologies have been developed to improve the efficiency of screening for esophageal squamous cell carcinoma (ESCC). Here, we developed and validated a novel system of computer-aided detection (CAD) using a deep neural network (DNN) to localize and identify early ESCC under conventional endoscopic white-light imaging. METHODS: We collected 2428 (1332 abnormal, 1096 normal) esophagoscopic images from 746 patients to set up a novel DNN-CAD system in 2 centers and prepared a validation dataset containing 187 images from 52 patients. Sixteen endoscopists (senior, mid-level, and junior) were asked to review the images of the validation set. The diagnostic results, including accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were compared between the DNN-CAD system and endoscopists. RESULTS: The receiver operating characteristic curve for DNN-CAD showed that the area under the curve was >96%. For the validation dataset, DNN-CAD had a sensitivity, specificity, accuracy, PPV, and NPV of 97.8%, 85.4%, 91.4%, 86.4%, and 97.6%, respectively. The senior group achieved an average diagnostic accuracy of 88.8%, whereas the junior group had a lower value of 77.2%. After referring to the results of DNN-CAD, the average diagnostic ability of the endoscopists improved, especially in terms of sensitivity (74.2% vs 89.2%), accuracy (81.7% vs 91.1%), and NPV (79.3% vs 90.4%). CONCLUSIONS: The novel DNN-CAD system used for screening of early ESCC has high accuracy and sensitivity, and can help endoscopists to detect lesions previously ignored under white-light imaging.

Population Density and Risk of Inflammatory Bowel Disease: A Prospective Population-Based Study in 13 Countries or Regions in Asia-Pacific.

INTRODUCTION: Living in an urban environment may increase the risk of developing inflammatory bowel disease (IBD). It is unclear if this observation is seen globally. We conducted a population-based study to assess the relationship between urbanization and incidence of IBD in the Asia-Pacific region. METHODS: Newly diagnosed IBD cases between 2011 and 2013 from 13 countries or regions in Asia-Pacific were included. Incidence was calculated with 95% confidence interval (CI) and pooled using random-effects model. Meta-regression analysis was used to assess incidence rates and their association with population density, latitude, and longitude. RESULTS: We identified 1175 ulcerative colitis (UC), 656 Crohn's disease (CD), and 37 IBD undetermined (IBD-U). Mean annual IBD incidence per 100 000 was 1.50 (95% CI: 1.43-1.57). India (9.31; 95% CI: 8.38-10.31) and China (3.64; 95% CI, 2.97-4.42) had the highest IBD incidence in Asia. Incidence of overall IBD (incidence rate ratio [IRR]: 2.19; 95% CI: 1.01-4.76]) and CD (IRR: 3.28; 95% CI: 1.83-9.12) was higher across 19 areas of Asia with a higher population density. In China, incidence of IBD (IRR: 2.37; 95% CI: 1.10-5.16) and UC (IRR: 2.63; 95% CI: 1.2-5.8) was positively associated with gross domestic product. A south-to-north disease gradient (IRR: 0.94; 95% CI: 0.91-0.98) was observed for IBD incidence and a west-to-east gradient (IRR: 1.14; 95% CI: 1.05-1.24) was observed for CD incidence in China. This study received IRB approval. CONCLUSIONS: Regions in Asia with a high population density had a higher CD and UC incidence. Coastal areas within China had higher IBD incidence. With increasing urbanization and a shift from rural areas to cities, disease incidence may continue to climb in Asia.

Endoscopic and Histological Mucosal Healing in Ulcerative Colitis in the First Year of Diagnosis: Results from a Population-based Inception Cohort from Six Countries in Asia.

BACKGROUND AND AIMS: Mucosal healing is associated with improved long-term clinical outcomes in patients with ulcerative colitis. This population-based study assessed endoscopic and histological mucosal healing within the first year of diagnosis. METHODS: Consecutive patients diagnosed with ulcerative colitis from six countries in Asia were prospectively enrolled. Clinical demographics, blood markers and inflammatory activity were assessed at baseline. Mayo score and Nancy index were used to assess endoscopic and histological activities, respectively. Clinical, endoscopic and histological evaluations were repeated at 1 year. Logistic regression was performed to identify predictors of mucosal healing. RESULTS: Of 433 ulcerative colitis patients, 202 [46.7%] underwent colonoscopy at 1 year. In total, 68 [38.2%] achieved endoscopic mucosal healing and 35 [23.1%] achieved histological mucosal healing. On multivariate analysis, an elevated erythrocyte sedimentation rate [ESR] at diagnosis (odds ratio [OR], 0.332; 95% confidence interval (CI), 0.133-0.830; p = 0.018) was a significant negative predictor of endoscopic mucosal healing at 1 year, while histological features of ulceration [OR, 0.156; 95% CI, 0.028-0.862; p = 0.033] and being an ex-smoker [OR, 0.067; 95% CI, 0.005-0.965; p = 0.047] were significant negative predictors of histological healing at 1 year. Both endoscopic and histological mucosal healing were associated with less steroid use [p < 0.001 and p = 0.001, respectively] and hospitalization [p = 0.002 and p = 0.01, respectively]. CONCLUSIONS: Mucosal healing was achieved in fewer than half of patients with ulcerative colitis in the first year of diagnosis. An elevated ESR predicted less likelihood of endoscopic mucosal healing, while histological features of ulceration and being an ex-smoker at diagnosis predicted less likelihood of histological healing.

Early Course of Inflammatory Bowel Disease in a Population-Based Inception Cohort Study From 8 Countries in Asia and Australia.

BACKGROUND & AIMS: The incidence of inflammatory bowel disease (IBD) is increasing in Asia, but little is known about disease progression in this region. The Asia-Pacific Crohn's and Colitis Epidemiology Study was initiated in 2011, enrolling subjects from 8 countries in Asia (China, Hong Kong, Indonesia, Sri Lanka, Macau, Malaysia, Singapore, and Thailand) and Australia. We present data from this ongoing study. METHODS: We collected data on 413 patients diagnosed with IBD (222 with ulcerative colitis [UC], 181 with Crohn's disease [CD], 10 with IBD unclassified; median age, 37 y) from 2011 through 2013. We analyzed the disease course and severity and mortality. Risks for medical and surgical therapies were assessed using Kaplan-Meier analysis. RESULTS: The cumulative probability that CD would change from inflammatory to stricturing or penetrating disease was 19.6%. The cumulative probabilities for use of immunosuppressants or anti-tumor necrosis factor agents were 58.9% and 12.0% for patients with CD, and 12.7% and 0.9% for patients with UC, respectively. Perianal CD was associated with an increased risk of anti-tumor necrosis factor therapy within 1 year of its diagnosis (hazard ratio, 2.97; 95% confidence interval, 1.09-8.09). The cumulative probabilities for surgery 1 year after diagnosis were 9.1% for patients with CD and 0.9% for patients with UC. Patients with CD and penetrating disease had a 7-fold increase for risk of surgery, compared with patients with inflammatory disease (hazard ratio, 7.67; 95% confidence interval, 3.93-14.96). The overall mortality for patients with IBD was 0.7%. CONCLUSIONS: In a prospective population-based study, we found that the early course of disease in patients with IBD in Asia was comparable with that of the West. Patients with CD frequently progress to complicated disease and have accelerated use of immunosuppressants. Few patients with early stage UC undergo surgery in Asia. Increasing our understanding of IBD progression in different populations can help optimize therapy and improve outcomes.

Environmental risk factors in inflammatory bowel disease: a population-based case-control study in Asia-Pacific.

OBJECTIVE: The rising incidence of inflammatory bowel disease in Asia supports the importance of environmental risk factors in disease aetiology. This prospective population-based case-control study in Asia-Pacific examined risk factors prior to patients developing IBD. DESIGN: 442 incident cases (186 Crohn's disease (CD); 256 UC; 374 Asians) diagnosed between 2011 and 2013 from eight countries in Asia and Australia and 940 controls (frequency-matched by sex, age and geographical location; 789 Asians) completed an environmental factor questionnaire at diagnosis. Unconditional logistic regression models were used to estimate adjusted ORs (aOR) and 95% CIs. RESULTS: In multivariate model, being breast fed >12 months (aOR 0.10; 95% CI 0.04 to 0.30), antibiotic use (aOR 0.19; 0.07 to 0.52), having dogs (aOR 0.54; 0.35 to 0.83), daily tea consumption (aOR 0.62; 0.43 to 0.91) and daily physical activity (aOR 0.58; 0.35 to 0.96) decreased the odds for CD in Asians. In UC, being breast fed >12 months (aOR 0.16; 0.08 to 0.31), antibiotic use (aOR 0.48; 0.27 to 0.87), daily tea (aOR 0.63; 0.46 to 0.86) or coffee consumption (aOR 0.51; 0.36 to 0.72), presence of hot water tap (aOR 0.65; 0.46 to 0.91) and flush toilet in childhood (aOR 0.71; 0.51 to 0.98) were protective for UC development whereas ex-smoking (aOR 2.02; 1.22 to 3.35) increased the risk of UC. CONCLUSIONS: This first population-based study of IBD risk factors in Asia-Pacific supports the importance of childhood immunological, hygiene and dietary factors in the development of IBD, suggesting that markers of altered intestinal microbiota may modulate risk of IBD later in life.

Incidence and phenotype of inflammatory bowel disease based on results from the Asia-pacific Crohn's and colitis epidemiology study.

BACKGROUND & AIMS: Inflammatory bowel diseases (IBD) are becoming more common in Asia, but epidemiologic data are lacking. The Asia-Pacific Crohn's and Colitis Epidemiology Study aimed to determine the incidence and phenotype of IBD in 8 countries across Asia and in Australia. METHODS: We performed a prospective, population-based study of IBD incidence in predefined catchment areas, collecting data for 1 year, starting on April 1, 2011. New cases were ascertained from multiple overlapping sources and entered into a Web-based database. Cases were confirmed using standard criteria. Local endoscopy, pathology, and pharmacy records were searched to ensure completeness of case capture. RESULTS: We identified 419 new cases of IBD (232 of ulcerative colitis [UC], 166 of Crohn's disease [CD], and 21 IBD-undetermined). The crude annual overall incidence values per 100,000 individuals were 1.37 for IBD in Asia (95% confidence interval: 1.25-1.51; 0.76 for UC, 0.54 for CD, and 0.07 for IBD-undetermined) and 23.67 in Australia (95% confidence interval: 18.46-29.85; 7.33 for UC, 14.00 for CD, and 2.33 for IBD-undetermined). China had the highest incidence of IBD in Asia (3.44 per 100,000 individuals). The ratios of UC to CD were 2.0 in Asia and 0.5 in Australia. Median time from symptom onset to diagnosis was 5.5 months (interquartile range, 1.4-15 months). Complicated CD (stricturing, penetrating, or perianal disease) was more common in Asia than Australia (52% vs 24%; P = .001), and a family history of IBD was less common in Asia (3% vs 17%; P < .001). CONCLUSIONS: We performed a large-scale population-based study and found that although the incidence of IBD varies throughout Asia, it is still lower than in the West. IBD can be as severe or more severe in Asia than in the West. The emergence of IBD in Asia will result in the need for specific health care resources, and offers a unique opportunity to study etiologic factors in developing nations.