PROJECTS circle image

Projects

Artificial intelligence enabled electrocardiography system

Participants

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Shih-Hua Lin
Adviser

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Chin Lin
Technical PI

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Chin-Sheng Lin
Clinical PI

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Wen-Hui Fang
Community PI

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Chia-Jung Hsu
Engineer

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Yu-Juei Hsu
Clinical PI

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Shi-Hung Tsai
Emergency PI

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Chia-Cheng Lee
IT manager

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Sy-Jou Chen
Emergency physician

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Wen-Yu Lin
Cardiologist

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Ban-Yen Liu
Cardiologist

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Wen-Cheng Liu
Cardiologist

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Da-Wei Chang
Cardiologist

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Yu-Lan Liu
Cardiologist

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Ping-Huang Tsai
Nephrologist

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Chiao-Hsiang Chang
Cardiologist

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Hung-Yi Chen
Cardiologist

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Chiao-Chin Lee
Cardiologist

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Wei-Ting Liu
Cardiologist

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Yu-Cheng Chen
Cardiologist

Summary

Cardiovascular disease stands as a critical public health concern, with screening and diagnosis playing a paramount role in alleviating the burden of these conditions. However, due to the constraints imposed by limited medical resources, Taiwan has yet to formulate an encompassing national-level cardiovascular disease management plan. Electrocardiography (ECG) offers a cost-effective and uncomplicated diagnostic tool, a potential heightened by the integration of deep learning techniques which have enabled the development of artificial intelligence-assisted ECG analysis systems capable of accurately aiding in the screening of chronic cardiovascular diseases and detecting acute cardiovascular events. There even exists the prospect of transitioning ECG testing from medical institutions to community and domestic settings, thus augmenting the accessibility of cardiovascular disease management. Presently, research pertaining to artificial intelligence-assisted ECG analysis systems predominantly revolves around retrospective model development studies. In the future, we intend to conduct intervention-based research across hospital, community, and home environments, in order to explore the benefits of employing artificial intelligence-assisted ECG analysis systems for cardiovascular diseases within these three domains. Our aspiration is to establish a nationwide cardiovascular disease screening strategy founded upon the artificial intelligence-assisted ECG analysis system, positioning Taiwan as an exemplar in international cardiovascular disease policy planning. Moreover, we propose a digital home care model encompassing diagnosed cardiovascular disease patients, potentially cultivating a novel precision medical and healthcare industry.

Selected references:
  1. AI-enabled electrocardiography alert intervention and all-cause mortality: a pragmatic randomized clinical trial. Nat Med, 30(5):1461–1470. [PDF]
  2. Artificial Intelligence–Powered Rapid Identification of ST-Elevation Myocardial Infarction via Electrocardiogram (ARISE) — A Pragmatic Randomized Controlled Trial. NEJM AI, 1(7):AIoa2400190. [PDF]
  3. Point-of-care artificial intelligence-enabled ECG for dyskalemia: a retrospective cohort analysis for accuracy and outcome prediction. npj Digital Medicine, 5(1):8. [PDF]
  4. A deep-learning algorithm for detecting acute myocardial infarction. EuroIntervention, 17(9):765–773. [PDF]
  5. Opportunistic Screening for Asymptomatic Left Ventricular Dysfunction Using Electrocardiographic Artificial Intelligence: A Cost-Effective Approach. Can J Cardiol, 40(7):1310–1321. [PDF]
  6. A Deep-Learning Algorithm (ECG12Net) for Detecting Hypokalemia and Hyperkalemia by Electrocardiography: Algorithm Development. JMIR Med Inform, 8(3):e15931. [PDF]
  7. We shared our works to the Discovery.

Algorithm development

Participants

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Chin Lin
Adviser

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Yu-Sheng Luo
Post doct

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Chun-Ho Lee
Master student

Summary

Deep learning is a data-driven algorithm, so it is necessary to understand the limitations of medical data and design suitable algorithms to build an accurate medical artificial intelligence system. We consider that main differences between medical field and other fields and solutions are as follows:
1. Unsupervised learning aided supervised learning to solve the rare data problem: Because the medical data involves manual operations and expensive instruments, the amount of labeled data is scarce compared with other industries. Moreover, the incidence and prevalence of more fatel diseases is lower compared with other common diseases. We need to face this important limitation rather than selecting sufficient data for research. Transfer learning is a common solution in deep learning training that can effectively reduce the number of samples required. Our main work is to use a large amount of unlabeled data for unsupervised algorithms, and apply them to supervised learning.1
2. Combination of multi-level statistical models and deep learning to create personalized artificial intelligence: The response of each indivial to the same treatment is different, so it is impossible to use one artificial intelligence models for accurately appling in everyone. Therefore, it is very important to establish a personalized artificial intelligence model, but it is difficult to train each person's data separately in medical field. The multi-level data analysis in statistics shows how to use the distribution assumptions to construct personalized predictions. Even if only one record also can be used to construct a personalized model, our research attempts to apply this concept to deep learning training. The new age medical artificial intelligence can dimamicly revise the prediction results.2
3. To design special prediction functions and loss functions: When using artificial intelligence models in the medical field, interpretability and confidence issues often arise. For example, if the data quality is poor, it may be advisable for frontline operators to perform the check again immediately. This is particularly important in prospective trials.3
4. Special accuracy evaluation metric: Medical artificial intelligence research often ignores the problem of variable dependence in medical data, such as the relationship between electrocardiograms and age and gender, and the relationship between gender and age and bone density, leading to confusion about whether the relationship between electrocardiograms and bone density is based on a real causal relationship. Because of the powerful feature extraction ability of deep learning models, AI currently often learns false correlations to establish predictive logic, which will reduce the accuracy of external validation in the future, so it is necessary to design appropriate evaluation indicators for this problem.4

Selected references:
  1. Extensive deep learning model to enhance electrocardiogram application via latent cardiovascular feature extraction. Comput Methods Programs Biomed, 231:107359. [PDF]
  2. Development and validation of dynamic deep learning algorithm using electrocardiogram to predict dyskalemias in patients with multiple visits. Eur Heart J-DH, ztac072. [PDF]
  3. Artificial intelligence-enabled electrocardiogram screens low left ventricular ejection fraction with a degree of confidence. Digital Health, 8:20552076221143249. [PDF]

Medical image analysis

Participants

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Chin Lin
Adviser

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Yi-Ying Wu
Hematologist

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Dung-Jang Tsai
Research fellow

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Yu-Sheng Luo
PhD student

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Ying-Chu Chen
MS student

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Xin-An Lin
Engineer

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Yu-Cheng Chen
Cardiologist

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Hao-Chun Liao
BS student

Summary

Machine learning has seen some dramatic developments recently, leading to a lot of interest from industry, academia and popular culture. These are driven by breakthroughs in artificial neural networks, often termed deep learning, a set of techniques and algorithms that enable computers to discover complicated patterns in large data sets. Feeding the breakthroughs is the increased access to data (“big data”), user-friendly software frameworks, and an explosion of the available compute power, enabling the use of neural networks that are deeper than ever before. These models nowadays form the state-of-the-art approach to a wide variety of problems in computer vision, language modeling and robotics. Deep learning rose to its prominent position in computer vision when neural networks started outperforming other methods on several high-profile image analysis benchmarks. Most famously on the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) in 2012 when a deep learning model (a convolutional neural network) halved the second best error rate on the image classification task. Enabling computers to recognize objects in natural images was until recently thought to be a very difficult task, but by now convolutional neural networks have surpassed even human performance on the ILSVRC, and reached a level where the ILSVRC classification task is essentially solved (i.e. with error rate close to the Bayes rate). Deep learning techniques have become the de facto standard for a wide variety of computer vision problems. Healthcare providers generate and capture enormous amounts of data containing extremely valuable signals and information, at a pace far surpassing what “traditional” methods of analysis can process. Machine learning therefore quickly enters the picture, as it is one of the best ways to integrate, analyze and make predictions based on large, heterogeneous data sets. Recently, we have developed an object detection model for bone marrow smear interpretation. The chest X-ray can also be used in cardiovascular diesaese diagnosis, such as heart failure, cardiopulmonary age, aortic dissection, sudden death, and osteoporosis, etc.

Selected references:
  1. Federated learning for predicting clinical outcomes in patients with COVID-19. Nat Med, 27(10):1735–1743. [PDF]
  2. Osteoporotic Precise Screening Using Chest Radiography and Artificial Neural Network: The OPSCAN Randomized Controlled Trial. Radiology, 311(3):e231937. [PDF]
  3. Detection of left ventricular systolic dysfunction using an artificial intelligence-enabled chest X-ray. Can J Cardiol, 38(6):763–773. [PDF]
  4. A Deep-Learning Algorithm-Enhanced System Integrating Electrocardiograms and Chest X-rays for Diagnosing Aortic Dissection. Can J Cardiol, 38(2):160–168. [PDF]

Medical Natural Language Processing

Participants

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Chin Lin
Adviser

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Wen-Hui Fang
Clinical expert

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Dung-Jang Tsai
Research fellow

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Yu-Sheng Luo
PhD student

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Xin-An Lin
Engineer

Summary

Automated extrating structured medical information methods are increasingly being researched because of the increasing volume and accessibility of electronic medical data. However, it is difficult to use because more than 80% information is hidden in free-text clinical narratives. The current methods for collecting this structured information usually involve manual identification, but manual identification of disease codes from free-text clinical narratives is laborious and costly. The traditional document classification algorithm is to use bag-of-words models and machine learning meclassifiers, such as SVM and Random Forest. However, it is not really accurate and cannot face emerging diseases. Our team has developed an automatic disease classification approach by word embedding combining convolutional neural network, and it increases 30% performance compared with traditional methods1. Moreover, it can handle emerging disease issue by external vocabulary resources. We can obtain more data-driven clues by Artificial Intelligence to help promote the progress of medicine. The health care field will then truly enter the age of big data2.This technology was also applied in the medical record scoring system3.

Selected references:
  1. Artificial Intelligence Learning Semantics via External Resources for Classifying Diagnosis Codes in Discharge Notes. J Med Internet Res, 19(11):e380. [PDF]
  2. Projection Word Embedding Model With Hybrid Sampling Training for Classifying ICD-10-CM Codes: Longitudinal Observational Study. JMIR Med Inform, 7(3):e14499. [PDF]