Artificial intelligence in hematology

Artificial intelligence (AI) is a computer based science which aims to simulate human brain faculties using a computational system. A brief history of this new science goes from the creation of the first artificial neuron in 1943 to the first artificial neural network application to genetic algorithms. The potential for a similar technology in medicine has immediately been identified by scientists and researchers. The possibility to store and process all medical knowledge has made this technology very attractive to assist or even surpass clinicians in reaching a diagnosis. Applications of AI in medicine include devices applied to clinical diagnosis in neurology and cardiopulmonary diseases, as well as the use of expert or knowledge-based systems in routine clinical use for diagnosis, therapeutic management and for prognostic evaluation. Biological applications include genome sequencing or DNA gene expression microarrays, modeling gene networks, analysis and clustering of gene expression data, pattern recognition in DNA and proteins, protein structure prediction. In the field of hematology the first devices based on AI have been applied to the routine laboratory data management. New tools concern the differential diagnosis in specific diseases such as anemias, thalassemias and leukemias, based on neural networks trained with data from peripheral blood analysis. A revolution in cancer diagnosis, including the diagnosis of hematological malignancies, has been the introduction of the first microarray based and bioinformatic approach for molecular diagnosis: a systematic approach based on the monitoring of simultaneous expression of thousands of genes using DNA microarray, independently of previous biological knowledge, analysed using AI devices. Using gene profiling, the traditional diagnostic pathways move from clinical to molecular based diagnostic systems.     

人工智能在内分泌代谢领域的应用及展望

人工智能(artificial intelligence,AI)是近年发展最快的科学之一,其在医学领域的发展带来了全新的概念,也对传统医学带来了巨大的冲击,是借势而为还是静观其变是对各学科和医学人的考验。AI在医学影像等领域已经取得了令人瞩目的效果。目前,AI在内分泌代谢领域的应用研发也日趋广泛,包括在糖尿病及其并发症...     

Artificial Intelligence-Powered Blockchains for Cardiovascular Medicine

Clinical databases, particularly those composed of big data, face growing security challenges. Blockchain, the open, decentralized, distributed public ledger technology powering cryptocurrency, records transactions securely without the need for third-party verification. In the health care setting, decentralized blockchain networks offer a secure interoperable gateway for clinical research and practice data. Here, we discuss recent advances and potential future directions for the application of blockchain and its integration with artificial intelligence (AI) in cardiovascular medicine. We first review the basic underlying concepts of this technology and contextualise it within the spectrum of current, well known applications. We then consider specific applications for cardiovascular medicine and research in areas such as high-throughput gene sequencing, wearable technologies, and clinical trials. We then evaluate current challenges to effective implementation and future directions. We also summarise the health care applications that can be realised by combining decentralized blockchain computing platforms (for data security) and AI computing (for data analytics). By leveraging high-performance computing and AI capable of securely managing large and rapidly expanding medical databases, blockchain incorporation can provide clinically meaningful predictions, help advance research methodology (eg, via robust AI-blockchain decentralized clinical trials), and provide virtual tools in clinical practice (eg, telehealth, sensory-based technologies, wearable medical devices). Integrating AI and blockchain approaches synergistically amplifies the strengths of both technologies to create novel solutions to serve the objective of providing precision cardiovascular medicine.     

A prediction model based on artificial neural networks for the diagnosis of obstructive sleep apnea

Background

Recently, artificial neural networks (ANNs) have been widely applied in science, engineering, and medicine. In the present study, we evaluated the ability of artificial neural networks to be used as a computer program and assistant tool in the diagnosis of obstructive sleep apnea (OSA). Our hypothesis was that ANNs could use clinical information to precisely predict cases of OSA.

Method

The study population in this clinical trial consisted of 201 patients with suspected OSA (140 with a positive diagnosis of OSA and 61 with a negative diagnosis of OSA). The artificial neural network was trained by assessing five clinical variables from 201 patients; efficiency was then estimated in this group of 201 patients. The patients were classified using a five-element input vector. ANN classifiers were assessed with the multilayer perceptron (MLP) networks.

Results

Use of the MLP classifiers resulted in a diagnostic accuracy of 86.6 %, which in clinical practice is high enough to reduce the number of patients evaluated by polysomnography (PSG), an expensive and limited diagnostic resource.

Conclusions

By establishing a pattern that allows the recognition of OSA, ANNs can be used to identify patients requiring PSG.

     

Application of Artificial Intelligence in the Detection and Characterization of Colorectal Neoplasm

Endoscpists always have tried to pursue a perfect colonoscopy, and application of artificial intelligence (AI) using deep-learning algorithms is one of the promising supportive options for detection and characterization of colorectal polyps during colonoscopy. Many retrospective studies conducted with real-time application of AI using convolutional neural networks have shown improved colorectal polyp detection. Moreover, a recent randomized clinical trial reported additional polyp detection with shorter analysis time. Studies conducted regarding polyp characterization provided additional promising results. Application of AI with narrow band imaging in real-time prediction of the pathology of diminutive polyps resulted in high diagnostic accuracy. In addition, application of AI with endocytoscopy or confocal laser endomicroscopy was investigated for real-time cellular diagnosis, and the diagnostic accuracy of some studies was comparable to that of pathologists. With AI technology, we can expect a higher polyp detection rate with reduced time and cost by avoiding unnecessary procedures, resulting in enhanced colonoscopy efficiency. However, for AI application in actual daily clinical practice, more prospective studies with minimized selection bias, consensus on standardized utilization, and regulatory approval are needed. (Gut Liver 2021;15:-353)     

Artificial intelligence technique in detection of early esophageal cancer

Due to the rapid progression and poor prognosis of esophageal cancer(EC), the early detection and diagnosis of early EC are of great value for the prognosis improvement of patients. However, the endoscopic detection of early EC, especially Barrett's dysplasia or squamous epithelial dysplasia, is difficult. Therefore, the requirement for more efficient methods of detection and characterization of early EC has led to intensive research in the field of artificial intelligence(AI). Deep learning(DL) has brought about breakthroughs in processing images, videos, and other aspects, whereas convolutional neural networks(CNNs) have shone lights on detection of endoscopic images and videos. Many studies on CNNs in endoscopic analysis of early EC demonstrate excellent performance including sensitivity and specificity and progress gradually from in vitro image analysis for classification to real-time detection of early esophageal neoplasia. When AI technique comes to the pathological diagnosis, borderline lesions that are difficult to determine may become easier than before. In gene diagnosis, due to the lack of tissue specificity of gene diagnostic markers, they can only be used as supplementary measures at present. In predicting the risk of cancer, there is still a lack of prospective clinical research to confirm the accuracy of the risk stratification model.     

人工神经网络在传染病研究中的应用

在信息技术的推动下,人工神经网络的应用越来越广泛。由于人工神经网络具有自适应性、并行处理能力和非线性等优点,逐渐被应用于医学和生物学领域的研究。本文对人工神经网络近年来在传染病相关因素、预测预报和诊断等方面的应用作一综述。     

DNA microarray analysis: principles and clinical impact

In recent years, a new technology, allowing the measurements of the expression of thousands of genes simultaneously, has emerged in medicine. This method, called DNA microarray analysis, is today one of the most promising method in functional genomics. Fundamental patterns in gene expression are extracted by several clustering methods like: hierarchical clustering, self organizing maps and support vector machines. Changes in gene expression, as a response to changing environment conditions, diseases, drug treatment or chemotherapy medications, can be detected allowing insights into the dynamic of the genome. Microarrays seem to be an important tool for diagnosis of diseases at a molecular level. Applications are for example the improvement of diagnosis and treatment of cancer and the improvement of the effectiveness of drug treatment. In this introductory paper, we present the principles of DNA microarray experiments, selected clustering methods for gene expression analysis and the impact to clinical research.     

血浆微小 RNA-223(miR-223)及叉头框蛋白3a水平与冠心病病人心脏结构变化的相关性研究

人工智能在心电图中的应用是心血管领域正在发生变革的一个重要方向。近年来,先进的人工智能技术,如深度学习,卷积神经网络等,已经实现了对心电图的快速、类似于人类的判读,而多层神经网络可以精确地检测到人类判读者基本无法识别的信号和模式,使心电图成为一个强大的“生物标志物”。大量的数字化心电图已经被用于开发人工智能模型,可检测阵发性心房颤动、左心室功能障碍、心肌病以及高钾血症、瓣膜疾病等异常情况。在这篇综述中,我们总结了人工智能辅助的心电图诊断在心血管疾病中的应用现状,讨论并评估了其临床意义、局限性和发展前景。     

Artificial neural networks distinguish among subtypes of neoplastic colorectal lesions

BACKGROUND & AIMS: There is a subtle distinction between sporadic colorectal adenomas and cancers (SAC) and inflammatory bowel disease (IBD)-associated dysplasias and cancers. However, this distinction is clinically important because sporadic adenomas are usually managed by polypectomy alone, whereas IBD-related high-grade dysplasias mandate subtotal colectomy. The current study evaluated the ability of artificial neural networks (ANNs) based on complementary DNA (cDNA) microarray data to discriminate between these 2 types of colorectal lesions. METHODS: We hybridized cDNA microarrays, each containing 8064 cDNA clones, to RNAs derived from 39 colorectal neoplastic specimens. Hierarchical clustering was performed, and an ANN was constructed and trained on a set of 5 IBD-related dysplasia or cancer (IBDNs) and 22 SACs. RESULTS: Hierarchical clustering based on all 8064 clones failed to correctly categorize the SACs and IBDNs. However, the ANN correctly diagnosed 12 of 12 blinded samples in a test set (3 IBDNs and 9 SACs). Furthermore, using an iterative process based on the computer programs GeneFinder, Cluster, and MATLAB, we reduced the number of clones used for diagnosis from 8064 to 97. Even with this reduced clone set, the ANN retained its capacity for correct diagnosis. Moreover, cluster analysis performed with these 97 clones now separated the 2 types of lesions. CONCLUSIONS: Our results suggest that ANNs have the potential to discriminate among subtly different clinical entities, such as IBDNs and SACs, as well as to identify gene subsets having the power to make these diagnostic distinctions.     

基于深度学习的医学图像分割方法

近年来,日渐成熟的人工智能深度学习技术使得众多领域逐渐实现自动化智能化作业。在医疗领域,随着医疗数据电子化和互联网医疗的发展,基于卷积神经网络实现包含定位、分割和分类于一体的辅助诊断系统应用已成为新型医疗模式发展的必然趋势。医学影像分割技术是医疗图像自动分析中的难点和重点,目前仍面临许多亟待解决的问题。该文将从临床医学影像的特点、深度学习主流分割网络和医学图像分割网络在临床中的应用3个方面对医学图像分割领域的研究进展进行系统综述,并进一步分析卷积神经网络在医学影像分割任务中的发展现状、面临的挑战以及未来的发展方向。     

The Future Role of High-Performance Computing in Cardiovascular Medicine and Science -Impact of Multi-Dimensional Data Analysis-

Advances in High-performance computing (HPC) technology have reached the capacity to inform cardiovascular (CV) science in the realm of both inductive and constructive approaches. Clinical trials allow for the comparison of the effect of an intervention without the need to understand the mechanism. This is a typical example of an inductive approach. In the HPC field, training an artificial intelligence (AI) model, constructed by neural networks, to predict future CV events with the use of large scale multi-dimensional datasets is the counterpart that may rely on as well as inform understanding of mechanistic underpinnings for optimization. However, in contrast to clinical trials, AI can calculate event risk at the individual level and has the potential to inform and refine the application of personalized medicine. Despite this clear strength, results from AI analyses may identify otherwise unidentified/unexpected (i.e. non-intuitive) relationships between multi-dimensional data and clinical outcomes that may further unravel potential mechanistic pathways and identify potential therapeutic targets, therebycontributing to the parsing of observational associations from causal links. The constructive approach will remain critical to overcome limitations of existing knowledge and anchored biases to actualize a more sophisticated understanding of the complex pathobiology of CV diseases. HPC technology has the potential to underpin this constructive approach in CV basic and clinical science. In general, even complex biological phenomena can be reduced to combinations of simple biological/chemical/physical laws. In the deductive approach, the focus/intent is to explain complex CV diseases by combinations of simple principles.     

Artificial intelligence in medical imaging of the liver

Artificial intelligence(AI), particularly deep learning algorithms, is gaining extensive attention for its excellent performance in image-recognition tasks. They can automatically make a quantitative assessment of complex medical image characteristics and achieve an increased accuracy for diagnosis with higher efficiency. AI is widely used and getting increasingly popular in the medical imaging of the liver, including radiology, ultrasound, and nuclear medicine. AI can assist physicians to make more accurate and reproductive imaging diagnosis and also reduce the physicians' workload. This article illustrates basic technical knowledge about AI, including traditional machine learning and deep learning algorithms, especially convolutional neural networks, and their clinical application in the medical imaging of liver diseases, such as detecting and evaluating focal liver lesions, facilitating treatment, and predicting liver treatment response. We conclude that machine-assisted medical services will be a promising solution for future liver medical care. Lastly, we discuss the challenges and future directions of clinical application of deep learning techniques.     

The potential role of machine learning in modelling advanced chronic liver disease

The use of artificial intelligence is rapidly increasing in medicine to support clinical decision making mostly through diagnostic and prediction models. Such models derive from huge databases (big data) including a large variety of health-related individual patient data (input) and the corresponding diagnosis and/or outcome (labels). Various types of algorithms (e.g. neural networks) based on powerful computational ability (machine), allow to detect the relationship between input and labels (learning). More complex algorithms, like recurrent neural network can learn from previous as well as actual input (deep learning) and are used for more complex tasks like imaging analysis and personalized (bespoke) medicine. The prompt availability of big data makes that artificial intelligence can provide rapid answers to questions that would require years of traditional clinical research. It may therefore be a key tool to overcome several major gaps in the model of advanced chronic liver disease, mostly transition from mild to clinically significant portal hypertension, the impact of acute decompensation and the role of further decompensation and treatment efficiency. However, several limitations of artificial intelligence should be overcome before its application in clinical practice. Assessment of the risk of bias, understandability of the black boxes developing the models and models’ validation are the most important areas deserving clarification for artificial intelligence to be widely accepted from physicians and patients.     

Artificial intelligence: Emerging player in the diagnosis and treatment of digestive disease

Given the breakthroughs in key technologies, such as image recognition, deep learning and neural networks, artificial intelligence (AI) continues to be increasingly developed, leading to closer and deeper integration with an increasingly data-, knowledge- and brain labor-intensive medical industry. As society continues to advance and individuals become more aware of their health needs, the problems associated with the aging of the population are receiving increasing attention, and there is an urgent demand for improving medical technology, prolonging human life and enhancing health. Digestive system diseases are the most common clinical diseases and are characterized by complex clinical manifestations and a general lack of obvious symptoms in the early stage. Such diseases are very difficult to diagnose and treat. In recent years, the incidence of diseases of the digestive system has increased. As AI applications in the field of health care continue to be developed, AI has begun playing an important role in the diagnosis and treatment of diseases of the digestive system. In this paper, the application of AI in assisted diagnosis and the application and prospects of AI in malignant and benign digestive system diseases are reviewed.     

Molecular biology and gene therapy in gastroenterology and hepatology.

Molecular analyses have become an integral part of biomedical research as well as clinical medicine. The definition of the molecular and genetic basis of many human diseases has led to a better understanding of their pathogenesis and has in addition offered new perspectives for their diagnosis, therapy and prevention. Genetically, human diseases can be classified as monogenetic, complex genetic and acquired genetic diseases. Based on this classification, gene therapy is based on four concepts: gene substitution, gene augmentation, block of gene expression or function as well as DNA vaccination. While recent developments are promising, various delivery, targeting and safety issues need to be addressed before gene therapy will enter clinical practice. In the future, molecular diagnosis and gene therapy of gastrointestinal and liver diseases will be part of our patient management and complement existing diagnostic, therapeutic and preventive strategies.     

Performance of a deep learning-based identification system for esophageal cancer from CT images

Background

Because cancers of hollow organs such as the esophagus are hard to detect even by the expert physician, it is important to establish diagnostic systems to support physicians and increase the accuracy of diagnosis. In recent years, deep learning-based artificial intelligence (AI) technology has been employed for medical image recognition. However, no optimal CT diagnostic system employing deep learning technology has been attempted and established for esophageal cancer so far.

Purpose

To establish an AI-based diagnostic system for esophageal cancer from CT images.

Materials and methods

In this single-center, retrospective cohort study, 457 patients with primary esophageal cancer referred to our division between 2005 and 2018 were enrolled. We fine-tuned VGG16, an image recognition model of deep learning convolutional neural network (CNN), for the detection of esophageal cancer. We evaluated the diagnostic accuracy of the CNN using a test data set including 46 cancerous CT images and 100 non-cancerous images and compared it to that of two radiologists.

Results

Pre-treatment esophageal cancer stages of the patients included in the test data set were clinical T1 (12 patients), clinical T2 (9 patients), clinical T3 (20 patients), and clinical T4 (5 patients). The CNN-based system showed a diagnostic accuracy of 84.2%, F value of 0.742, sensitivity of 71.7%, and specificity of 90.0%.

Conclusions

Our AI-based diagnostic system succeeded in detecting esophageal cancer with high accuracy. More training with vast datasets collected from multiples centers would lead to even higher diagnostic accuracy and aid better decision making.

     

Molecular diagnosis of lymphoid malignancies by gene expression profiling

Gene expression profiling using DNA microarrays has great potential to improve the understanding, diagnosis, and management of lymphomas, leukemias, and other malignancies. Gene expression profiling studies of diffuse large B-cell lymphoma (DLBCL) have shown that this diagnostic category encompasses at least two molecularly distinct diseases, differing in differentiation stage (cell of origin), oncogenic mechanisms, and clinical outcome. Gene expression profiling revealed that the antiapoptotic NF-kappaB pathway is constitutively active in one DLBCL subgroup, termed activated B cell-like DLBCL, and subsequent studies validated NF-kappaB as a therapeutic target in this type of lymphoma. DNA microarray studies of chronic lymphocytic leukemia (CLL) have led to a gene expression-based predictor that identifies two subtypes of CLL that differ with respect to clinical course and presence of immunoglobulin gene mutations in the CLL cells. These findings underscore the value of gene expression profiling in defining subtypes within the lymphoid malignancies that are molecularly and clinically distinct and argue that this genomic technology should become an integral part of prospective clinical trials.     

DNA microarrays for assessing ovarian cancer gene expression

Although DNA microarray analysis is presented as a revolution in gene expression studies, it is in fact based on the classic technique of Southern DNA hybridisation where a labelled DNA probe is hybridised to single stranded DNA that is bound to a solid support matrix. The truly revolutionary aspect of microarray analysis lies in the fact that, within a given cell population, the expression of tens of thousands of genes, and ultimately the entire genome, can be assayed simultaneously. This capability, when coupled with powerful data analysis software, allows researchers to rapidly compare gene expression between two cell populations. In the cancer field, this enables researchers to compare gene expression between normal and malignant cells and to identify genes that are differentially regulated during cancer development. Microarray data can also be used to categorize tumours on the basis of their molecular profile, which may provide important biological, diagnostic and prognostic information. As little as 5 years ago identifying even a few differentially expressed genes may have taken several years and cost tens of thousands of dollars. Today microarrays can identify ten times the number of candidate genes in just a few months and at a tenth of the cost. Even so, microarray analysis is still in its infancy and the technology is advancing rapidly. There is little doubt that microarrays will revolutionize our ability to quantify the complex changes that occur in gene expression during cancer development. The greatest challenge that lies ahead is how to translate this knowledge into clinically useful diagnostic and therapeutic tools. In this review, we describe the technical aspects of DNA microarray analysis and some of the current and future applications of this technology for analysing gene expression in ovarian cancer.