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Deep learning and liver disease

#artificialintelligence

Many medical imaging techniques have played a pivotal role in the early detection, diagnosis, and treatment of diseases, such as computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, positron emission tomography (PET), mammography, and X-ray. AI has made significant progress which allows machines to automatically represent and explain complicated data. It is widely applied in the medical field, especially in some domains that need imaging data analysis. According to Vivantil et al by using deep learning models based on longitudinal liver CT studies, new liver tumours could be detected automatically with a true positive rate of 86%, while the stand-alone detection rate was only 72% and this method achieved a precision of 87% and an improvement of 39% over the traditional SVM mode. CNN models which use ultrasound images to detect liver lesions were also developed. According to Liu et al by using a CNN model based on liver ultrasound images, the proposed method can effectively extract the liver capsules and accurately diagnose liver cirrhosis, with the diagnostic AUC being able to reach 0.968.


Machine Learning Approaches for Inferring Liver Diseases and Detecting Blood Donors from Medical Diagnosis

arXiv.org Machine Learning

For a medical diagnosis, health professionals use different kinds of pathological ways to make a decision for medical reports in terms of patients medical condition. In the modern era, because of the advantage of computers and technologies, one can collect data and visualize many hidden outcomes from them. Statistical machine learning algorithms based on specific problems can assist one to make decisions. Machine learning data driven algorithms can be used to validate existing methods and help researchers to suggest potential new decisions. In this paper, multiple imputation by chained equations was applied to deal with missing data, and Principal Component Analysis to reduce the dimensionality. To reveal significant findings, data visualizations were implemented. We presented and compared many binary classifier machine learning algorithms (Artificial Neural Network, Random Forest, Support Vector Machine) which were used to classify blood donors and non-blood donors with hepatitis, fibrosis and cirrhosis diseases. From the data published in UCI-MLR [1], all mentioned techniques were applied to find one better method to classify blood donors and non-blood donors (hepatitis, fibrosis, and cirrhosis) that can help health professionals in a laboratory to make better decisions. Our proposed ML-method showed better accuracy score (e.g. 98.23% for SVM). Thus, it improved the quality of classification.


Combining AI and biology could solve drug discovery's biggest problems

#artificialintelligence

Daphne Koller is best known as the cofounder of Coursera, the open database for online learning that launched in 2012. But before her work on Coursera, she was doing something much different. In 2000, Koller started working on applying machine learning to biomedical data sets to understand gene activity across cancer types. She put that work on hold to nurture Coursera, which took many more years than she initially thought it would. She didn't return to biology until 2016 when she joined Alphabet's life science research and development arm Calico.


12 Real-World Applications of Machine Learning in Healthcare

#artificialintelligence

According to news, Machine Learning is one of the most prominent technology for the future of the Healthcare industry. Is there any significant value, or is it just optimistic forecasts? In this article, you will learn on some practical implementations of the technology, as well as some on-point predictions. Today, technology-enabled healthcare is a reality as smart medical devices become a widespread thing. The healthcare industry welcomes the innovation; that's why the future of AI in healthcare is very bright.


Diagnosis of liver disease using computer-assisted imaging techniques: A Review

arXiv.org Machine Learning

The evidence says that liver disease detection using CAD is one of the most efficient techniques but the presence of better organization of studies and the performance parameters to represent the result analysis of the proposed techniques are pointedly missing in most of the recent studies. Few benchmarked studies have been found in some of the papers as benchmarking makes a reader understand that under which circumstances their experimental results or outcomes are better and useful for the future implementation and adoption of the work. Liver diseases and image processing algorithms, especially in medicine, are the most important and important topics of the day. Unfortunately, the necessary data and data, as they are invoked in the articles, are low in this area and require the revision and implementation of policies in order to gather and do more research in this field. Detection with ultrasound is quite normal in liver diseases and depends on the physician's experience and skills. CAD systems are very important for doctors to understand medical images and improve the accuracy of diagnosing various diseases. In the following, we describe the techniques used in the various stages of a CAD system, namely: extracting features, selecting features, and classifying them. Although there are many techniques that are used to classify medical images, it is still a challenging issue for creating a universally accepted approach.


Electronic Nose Technology Can Diagnose Diseases and Disorders

#artificialintelligence

The world is an increasingly busy place, and scheduling that appointment with your doctor for a check up might be on the end of your to-do list. As new technologies enter into the world of health and medicine, it is becoming increasingly easier to check your own vitals to ensure you're living the healthiest lifestyle possible. Enter IEEE Member Yangong Zheng and the electronic nose technology being used to detect and diagnose diseases and disorders simply through the smell your body emits. Although the biological olfactory system was inspiration for this type of technology, an electronic nose does not physically look like or depict a human nose. "Artificial systems for noninvasive chemical sensing are commonly referred to as'electronic nose technology'," explains Zheng.


Study shows artificial intelligence can detect language problems tied to liver failure

#artificialintelligence

Natural language processing, the technology that lets computers read, decipher, understand and make sense of human language, is the driving force behind internet search engines, email filters, digital assistants such as Amazon's Alexa and Apple's Siri, and language-to-language translation apps. Now, Johns Hopkins Medicine researchers say they have given this technology a new job as a clinical detective, diagnosing the early and subtle signs of language-associated cognitive impairments in patients with failing livers. They also report finding evidence that cognitive functioning is likely to be restored following a liver transplant. In a new paper in the journal npj Digital Medicine (formerly Nature Digital Medicine), the researchers describe how they used natural language processing, or NLP, to evaluate electronic message samples from patients with end-stage liver disease (ESLD), also known as chronic liver failure. ESLD has been associated with transient cognitive abnormalities such as diminished attention span, loss of memory and reduced psychomotor speed, an individual's ability to detect and respond to the world around them.


Digital Twin approach to Clinical DSS with Explainable AI

arXiv.org Artificial Intelligence

We propose a digital twin approach to improve healthcare decision support systems with a combination of domain knowledge and data. Domain knowledge helps build decision thresholds that doctors can use to determine a risk or recommend a treatment or test based on the specific patient condition. However, these assessments tend to be highly subjective and differ from doctor to doctor and from patient to patient. We propose a system where we collate this subjective risk by compiling data from different doctors treating different patients and build a machine learning model that learns from this knowledge. Then using state-of-the-art explainability concepts we derive explanations from this model. These explanations give us a summary of different doctor domain knowledge applied in different cases to give a more generic perspective. Also these explanations are specific to a particular patient and are customized for their condition. This is a form of a digital twin for the patient that can now be used to enhance decision boundaries for earlier defined decision tables that help in diagnosis. We will show an example of running this analysis for a liver disease risk diagnosis.


Hybrid Adaptive Neuro-Fuzzy Inference System for Diagnosing the Liver Disorders

arXiv.org Artificial Intelligence

In this study, a hybrid method based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) and Particle Swarm Optimization (PSO) for diagnosing Liver disorders (ANFIS-PSO) is introduced. This smart diagnosis method deals with a combination of making an inference system and optimization process which tries to tune the hyper-parameters of ANFIS based on the data-set. The Liver diseases characteristics are taken from the UCI Repository of Machine Learning Databases. The number of these characteristic attributes are 7, and the sample number is 354. The right diagnosis performance of the ANFIS-PSO intelligent medical system for liver disease is evaluated by using classification accuracy, sensitivity and specificity analysis, respectively. According to the experimental results, the performance of ANFIS-PSO can be more considerable than traditional FIS and ANFIS without optimization phase.


Multi-Task Survival Analysis of Liver Transplantation Using Deep Learning

AAAI Conferences

In this paper, we present the application of deep learning techniques to develop a modern model for the prediction of graft failure and survival analysis in liver transplant patients. We trained our model using the United Network for Organ Sharing (UNOS) dataset consisting of 59,115 patients from year 2002 to 2016 with around 150 features each. We also compare our model against an- other dataset – Scientific Registry of Transplant Recipients (SRTR) including 87,334 patients from year 2002 to 2018 – after selecting features by mapping them from UNOS data. Some of the most important features common to both datasets are Model for End-stage Liver Disease (MELD) score, patient body mass index (BMI), donor and patient age, cold ischemia time, and levels of various chemicals within the patient. To provide an additional tool to clinical practitioners in the allocation of a scarce resource, we developed a multi-task model to learn the survival function of a donor-recipient pair and hence predict the exact time of failure which outper- forms the traditional cox hazard models. The multi-task model produces very promising C-index results of 0.82 and 0.57 on the SRTR and UNOS datasets respectively.