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Wang Y, Ni H, Zhou J, Liu L, Lin J, Yin M, Gao J, Zhu S, Yin Q, Zhu J, Li R. A Semi-Supervised Learning Framework for Classifying Colorectal Neoplasia Based on the NICE Classification. J Imaging Inform Med 2024:10.1007/s10278-024-01123-9. [PMID: 38653910 DOI: 10.1007/s10278-024-01123-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Revised: 04/02/2024] [Accepted: 04/12/2024] [Indexed: 04/25/2024]
Abstract
Labelling medical images is an arduous and costly task that necessitates clinical expertise and large numbers of qualified images. Insufficient samples can lead to underfitting during training and poor performance of supervised learning models. In this study, we aim to develop a SimCLR-based semi-supervised learning framework to classify colorectal neoplasia based on the NICE classification. First, the proposed framework was trained under self-supervised learning using a large unlabelled dataset; subsequently, it was fine-tuned on a limited labelled dataset based on the NICE classification. The model was evaluated on an independent dataset and compared with models based on supervised transfer learning and endoscopists using accuracy, Matthew's correlation coefficient (MCC), and Cohen's kappa. Finally, Grad-CAM and t-SNE were applied to visualize the models' interpretations. A ResNet-backboned SimCLR model (accuracy of 0.908, MCC of 0.862, and Cohen's kappa of 0.896) outperformed supervised transfer learning-based models (means: 0.803, 0.698, and 0.742) and junior endoscopists (0.816, 0.724, and 0.863), while performing only slightly worse than senior endoscopists (0.916, 0.875, and 0.944). Moreover, t-SNE showed a better clustering of ternary samples through self-supervised learning in SimCLR than through supervised transfer learning. Compared with traditional supervised learning, semi-supervised learning enables deep learning models to achieve improved performance with limited labelled endoscopic images.
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Affiliation(s)
- Yu Wang
- Department of Hepatobiliary Surgery, Jintan Affiliated Hospital of Jiangsu University, Changzhou, Jiangsu, 213200, China
| | - Haoxiang Ni
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, # 899 Pinghai St., Suzhou, Jiangsu, 215006, China
- Suzhou Clinical Center of Digestive Disease, Suzhou, Jiangsu, 215006, China
| | - Jielu Zhou
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, # 899 Pinghai St., Suzhou, Jiangsu, 215006, China
- Department of Geriatrics, Kowloon Affiliated Hospital of Shanghai Jiao Tong University, Suzhou, Jiangsu, 215006, China
| | - Lihe Liu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, # 899 Pinghai St., Suzhou, Jiangsu, 215006, China
- Suzhou Clinical Center of Digestive Disease, Suzhou, Jiangsu, 215006, China
| | - Jiaxi Lin
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, # 899 Pinghai St., Suzhou, Jiangsu, 215006, China
- Suzhou Clinical Center of Digestive Disease, Suzhou, Jiangsu, 215006, China
| | - Minyue Yin
- Department of Gastroenterology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China
- National Clinical Research Center for Digestive Disease, Beijing Digestive Disease Center, State Key Laboratory of Digestive Health, Beijing, 100050, China
| | - Jingwen Gao
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, # 899 Pinghai St., Suzhou, Jiangsu, 215006, China
- Suzhou Clinical Center of Digestive Disease, Suzhou, Jiangsu, 215006, China
| | - Shiqi Zhu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, # 899 Pinghai St., Suzhou, Jiangsu, 215006, China
- Suzhou Clinical Center of Digestive Disease, Suzhou, Jiangsu, 215006, China
| | - Qi Yin
- Department of Anesthesiology, Jintan Affiliated Hospital of Jiangsu University, Changzhou, Jiangsu, 213200, China
| | - Jinzhou Zhu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, # 899 Pinghai St., Suzhou, Jiangsu, 215006, China.
- Suzhou Clinical Center of Digestive Disease, Suzhou, Jiangsu, 215006, China.
- Key Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, China.
| | - Rui Li
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, # 899 Pinghai St., Suzhou, Jiangsu, 215006, China.
- Suzhou Clinical Center of Digestive Disease, Suzhou, Jiangsu, 215006, China.
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Liu L, Lin J, Yin M, Liu L, Gao J, Liu X, Zhu J, Wu A. Association of the fat mass index with hepatic steatosis and fibrosis: evidence from NHANES 2017-2018. Sci Rep 2024; 14:6943. [PMID: 38521854 PMCID: PMC10960854 DOI: 10.1038/s41598-024-57388-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Accepted: 03/18/2024] [Indexed: 03/25/2024] Open
Abstract
Limited population-based studies discuss the association between fat mass index (FMI) and the risk of liver diseases. This investigation utilized data from the National Health and Nutrition Examination Survey (NHANES) to examine the linkage between the FMI and liver conditions, specifically steatosis and fibrosis. The study leveraged data from NHANES's 2017-2018 cross-sectional study, employing an oversampling technique to deal with sample imbalance. Hepatic steatosis and fibrosis were identified by vibration-controlled transient elastography. Receiver operating curve was used to assess the relationship of anthropometric indicators, e.g., the FMI, body mass index (BMI), weight-adjusted-waist index (WWI), percentage of body fat (BF%), waist-to-hip ratio (WHR), and appendicular skeletal muscle index (ASMI), with hepatic steatosis and fibrosis. In this study, which included 2260 participants, multivariate logistic regression models, stratified analyses, restricted cubic spline (RCS), and sharp regression discontinuity analyses were utilized. The results indicated that the WHR and the FMI achieved the highest area under the curve for identifying hepatic steatosis and fibrosis, respectively (0.720 and 0.726). Notably, the FMI presented the highest adjusted odds ratio for both hepatic steatosis (6.40 [4.91-8.38], p = 2.34e-42) and fibrosis (6.06 [5.00, 7.37], p = 5.88e-74). Additionally, potential interaction effects were observed between the FMI and variables such as the family income-to-poverty ratio, smoking status, and hypertension, all of which correlated with the presence of liver fibrosis (p for interaction < 0.05). The RCS models further confirmed a significant positive correlation of the FMI with the controlled attenuation parameter and liver stiffness measurements. Overall, the findings underscore the strong link between the FMI and liver conditions, proposing the FMI as a potential straightforward marker for identifying liver diseases.
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Affiliation(s)
- Lihe Liu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China
- Suzhou Clinical Center of Digestive Diseases, Suzhou, 215006, China
| | - Jiaxi Lin
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China
- Suzhou Clinical Center of Digestive Diseases, Suzhou, 215006, China
| | - Minyue Yin
- Department of Gastroenterology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China
| | - Lu Liu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China
- Suzhou Clinical Center of Digestive Diseases, Suzhou, 215006, China
| | - Jingwen Gao
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China
- Suzhou Clinical Center of Digestive Diseases, Suzhou, 215006, China
| | - Xiaolin Liu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China
- Suzhou Clinical Center of Digestive Diseases, Suzhou, 215006, China
| | - Jinzhou Zhu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China.
- Key Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, China.
| | - Airong Wu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China.
- Suzhou Clinical Center of Digestive Diseases, Suzhou, 215006, China.
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Lin J, Yang J, Yin M, Tang Y, Chen L, Xu C, Zhu S, Gao J, Liu L, Liu X, Gu C, Huang Z, Wei Y, Zhu J. Development and Validation of Multimodal Models to Predict the 30-Day Mortality of ICU Patients Based on Clinical Parameters and Chest X-Rays. J Imaging Inform Med 2024:10.1007/s10278-024-01066-1. [PMID: 38448758 DOI: 10.1007/s10278-024-01066-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 02/21/2024] [Accepted: 02/22/2024] [Indexed: 03/08/2024]
Abstract
We aimed to develop and validate multimodal ICU patient prognosis models that combine clinical parameters data and chest X-ray (CXR) images. A total of 3798 subjects with clinical parameters and CXR images were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and an external hospital (the test set). The primary outcome was 30-day mortality after ICU admission. Automated machine learning (AutoML) and convolutional neural networks (CNNs) were used to construct single-modal models based on clinical parameters and CXR separately. An early fusion approach was used to integrate both modalities (clinical parameters and CXR) into a multimodal model named PrismICU. Compared to the single-modal models, i.e., the clinical parameter model (AUC = 0.80, F1-score = 0.43) and the CXR model (AUC = 0.76, F1-score = 0.45) and the scoring system APACHE II (AUC = 0.83, F1-score = 0.77), PrismICU (AUC = 0.95, F1 score = 0.95) showed improved performance in predicting the 30-day mortality in the validation set. In the test set, PrismICU (AUC = 0.82, F1-score = 0.61) was also better than the clinical parameters model (AUC = 0.72, F1-score = 0.50), CXR model (AUC = 0.71, F1-score = 0.36), and APACHE II (AUC = 0.62, F1-score = 0.50). PrismICU, which integrated clinical parameters data and CXR images, performed better than single-modal models and the existing scoring system. It supports the potential of multimodal models based on structured data and imaging in clinical management.
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Affiliation(s)
- Jiaxi Lin
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, 188 Shizi Street, Jiangsu, Suzhou 215006, China
- Suzhou Clinical Center of Digestive Diseases, Suzhou, China
| | - Jin Yang
- Department of Critical Care Medicine, The First Affiliated Hospital of Soochow University, 188 Shizi Street, Jiangsu, Suzhou 215006, China
| | - Minyue Yin
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, 188 Shizi Street, Jiangsu, Suzhou 215006, China
- Suzhou Clinical Center of Digestive Diseases, Suzhou, China
| | - Yuxiu Tang
- Department of Critical Care Medicine, The First Affiliated Hospital of Soochow University, 188 Shizi Street, Jiangsu, Suzhou 215006, China
| | - Liquan Chen
- Department of Critical Care Medicine, The First Affiliated Hospital of Soochow University, 188 Shizi Street, Jiangsu, Suzhou 215006, China
| | - Chang Xu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, 188 Shizi Street, Jiangsu, Suzhou 215006, China
- Suzhou Clinical Center of Digestive Diseases, Suzhou, China
| | - Shiqi Zhu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, 188 Shizi Street, Jiangsu, Suzhou 215006, China
- Suzhou Clinical Center of Digestive Diseases, Suzhou, China
| | - Jingwen Gao
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, 188 Shizi Street, Jiangsu, Suzhou 215006, China
- Suzhou Clinical Center of Digestive Diseases, Suzhou, China
| | - Lu Liu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, 188 Shizi Street, Jiangsu, Suzhou 215006, China
- Suzhou Clinical Center of Digestive Diseases, Suzhou, China
| | - Xiaolin Liu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, 188 Shizi Street, Jiangsu, Suzhou 215006, China
- Suzhou Clinical Center of Digestive Diseases, Suzhou, China
| | - Chenqi Gu
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Zhou Huang
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Yao Wei
- Department of Critical Care Medicine, The First Affiliated Hospital of Soochow University, 188 Shizi Street, Jiangsu, Suzhou 215006, China.
| | - Jinzhou Zhu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, 188 Shizi Street, Jiangsu, Suzhou 215006, China.
- Suzhou Clinical Center of Digestive Diseases, Suzhou, China.
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Zhu S, Gao J, Liu L, Yin M, Lin J, Xu C, Xu C, Zhu J. Public Imaging Datasets of Gastrointestinal Endoscopy for Artificial Intelligence: a Review. J Digit Imaging 2023; 36:2578-2601. [PMID: 37735308 PMCID: PMC10584770 DOI: 10.1007/s10278-023-00844-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 05/03/2023] [Accepted: 05/03/2023] [Indexed: 09/23/2023] Open
Abstract
With the advances in endoscopic technologies and artificial intelligence, a large number of endoscopic imaging datasets have been made public to researchers around the world. This study aims to review and introduce these datasets. An extensive literature search was conducted to identify appropriate datasets in PubMed, and other targeted searches were conducted in GitHub, Kaggle, and Simula to identify datasets directly. We provided a brief introduction to each dataset and evaluated the characteristics of the datasets included. Moreover, two national datasets in progress were discussed. A total of 40 datasets of endoscopic images were included, of which 34 were accessible for use. Basic and detailed information on each dataset was reported. Of all the datasets, 16 focus on polyps, and 6 focus on small bowel lesions. Most datasets (n = 16) were constructed by colonoscopy only, followed by normal gastrointestinal endoscopy and capsule endoscopy (n = 9). This review may facilitate the usage of public dataset resources in endoscopic research.
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Affiliation(s)
- Shiqi Zhu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, 188 Shizi Street, Suzhou , Jiangsu, 215000, China
- Suzhou Clinical Center of Digestive Diseases, Suzhou, 215000, China
| | - Jingwen Gao
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, 188 Shizi Street, Suzhou , Jiangsu, 215000, China
- Suzhou Clinical Center of Digestive Diseases, Suzhou, 215000, China
| | - Lu Liu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, 188 Shizi Street, Suzhou , Jiangsu, 215000, China
- Suzhou Clinical Center of Digestive Diseases, Suzhou, 215000, China
| | - Minyue Yin
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, 188 Shizi Street, Suzhou , Jiangsu, 215000, China
- Suzhou Clinical Center of Digestive Diseases, Suzhou, 215000, China
| | - Jiaxi Lin
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, 188 Shizi Street, Suzhou , Jiangsu, 215000, China
- Suzhou Clinical Center of Digestive Diseases, Suzhou, 215000, China
| | - Chang Xu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, 188 Shizi Street, Suzhou , Jiangsu, 215000, China
- Suzhou Clinical Center of Digestive Diseases, Suzhou, 215000, China
| | - Chunfang Xu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, 188 Shizi Street, Suzhou , Jiangsu, 215000, China.
- Suzhou Clinical Center of Digestive Diseases, Suzhou, 215000, China.
| | - Jinzhou Zhu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, 188 Shizi Street, Suzhou , Jiangsu, 215000, China.
- Suzhou Clinical Center of Digestive Diseases, Suzhou, 215000, China.
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Gao J, Xu C, Zhu J. Towards Personalized Medicine for Chronic Liver Disease. J Pers Med 2023; 13:1432. [PMID: 37888043 PMCID: PMC10608442 DOI: 10.3390/jpm13101432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Accepted: 09/23/2023] [Indexed: 10/28/2023] Open
Abstract
Chronic liver disease is a progressive deterioration of hepatic functions and a continuous process of inflammation, destruction, and regeneration of liver parenchyma, resulting in fibrosis and cirrhosis [...].
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Affiliation(s)
- Jingwen Gao
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China
- Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China
| | - Chunfang Xu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China
- Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China
| | - Jinzhou Zhu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China
- Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China
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