Intelligent Medicine
Volume 02 · Issue 02 · 2022
Intell Med
- Sections
- Review
- Research Article
- Guideline & Standard
This essay is the starting point of a new column in Intelligent Medicine that invites interdisciplinary perspectives on the social, ethical, legal, and responsibility aspects of the use of artificial intelligence (AI) in medicine and health care. Papers in this column will examine the practical, conceptual, and policy dimensions of the use of AI for health-related purposes from comparative and international perspectives. We invite contributions from around the world in all application areas of AI for health, including health care, health research, drug development, health care system management, as well as public health and public health surveillance. The column aims to provide a forum for reflective and critical scholarship that contributes to the ongoing academic and policy debates about the development, use, governance, and implications of AI in medical and health care settings.
To launch the column, we first provide an overview of recent approaches that have been developed to identify and address the effects and potential impacts of science and technology innovations on human societies and the environment. These include ethical, legal, and social implications/aspects (ELSI/A) research, responsible research and innovation (RRI), sustainability transitions research, and the use of international standard-setting instruments for responsible and open science issued by the United Nations Educational, Scientific, and Cultural Organization (UNESCO), the World Health Organization (WHO), and other international bodies. In Part Two of this essay, we discuss some of the central challenges that arise with regard to the integration of AI and big data analytics in medical and health care settings. This includes concerns regarding (i) the control, reliability, and trustworthiness of AI systems, (ii) privacy and surveillance, (iii) the impact of AI and automation on health care staff employment and the nature of clinical work, (iv) the effects of AI on health inequalities, justice, and access to medical care, and (v) challenges related to regulation and governance. We end the essay with a call for papers and a set of questions that could be relevant for future studies.
Ketamine, a noncompetitive N-methyl-D-aspartate (NMDA) receptor antagonist, has been exclusively used as an anesthetic in medicine and has led to new insights into the pathophysiology of neuropsychiatric disorders. Clinical studies have shown that low subanesthetic doses of ketamine produce antidepressant effects for individuals with depression. However, its use as a treatment for psychiatric disorders has been limited due to its reinforcing effects and high potential for diversion and misuse. Preclinical studies have focused on understanding the molecular mechanisms underlying ketamine’s antidepressant effects, but a precise mechanism had yet to be elucidated. Here we review different hypotheses for ketamine’s mechanism of action including the direct inhibition and disinhibition of NMDA receptors, aminomethylphosphonic acid receptors (AMPAR) activation, and heightened activation of monoaminergic systems. The proposed mechanisms are not mutually exclusive, and their combined influence may exert the observed structural and functional neural impairments. Long term use of ketamine induces brain structural, functional impairments, and neurodevelopmental effects in both rodents and humans. Its misuse has increased rapidly in the past 20 years and is one of the most common addictive drugs used in Asia. The proposed mechanisms of action and supporting neuroimaging data allow for the development of tools to identify 'biotypes’ of ketamine use disorder (KUD) using machine learning approaches, which could inform intervention and treatment.
Breast cancer is a widely occurring cancer in women worldwide and is related to high mortality. The objective of this review was to present several approaches to investigate the application of multiple algorithms based on machine learning (ML) approach and biosensors for early breast cancer detection. Automation is needed because biosensors and ML are needed to identify cancers based on microscopic images. ML aims to facilitate self-learning in computers. Rather than relying on explicit pre-programmed rules and models, it is based on identifying patterns in observed data and building models to predict outcomes. We have compared and analysed various types of algorithms such as fuzzy extreme learning machine-radial basis function (ELM-RBF), support vector machine (SVM), support vector regression (SVR), relevance vector machine (RVM), naive bayes, k-nearest neighbours algorithm (K-NN), decision tree (DT), artificial neural network (ANN), back-propagation neural network (BPNN), and random forest across different databases including images digitized from fine needle aspirations of breast masses, scanned film mammography, breast infrared images, MR images, data collected by using blood analyses, and histopathology image samples. The results were compared on performance metric elements like accuracy, precision, and recall. Further, we used biosensors to determine the presence of a specific biological analyte by transforming the cellular constituents of proteins, DNA, or RNA into electrical signals that can be detected and analysed. Here, we have compared the detection of different types of analytes such as HER2, miRNA 21, miRNA 155, MCF-7 cells, DNA, BRCA1, BRCA2, human tears, and saliva by using different types of biosensors including FET, electrochemical, and sandwich electrochemical, among others. Several biosensors use a different type of specification which is also discussed. The result of which is analysed on the basis of detection limit, linear ranges, and response time. Different studies and related articles were reviewed and analysed systematically, and those published from 2010 to 2021 were considered. Biosensors and ML both have the potential to detect breast cancer quickly and effectively.
Tumor sprouting can reflect independent risk factors for tumor malignancy and a poor clinical prognosis. However, there are significant differences and difficulties associated with manually identifying tumor sprouting. This study used the Faster region convolutional neural network (RCNN) model to build a colorectal cancer tumor sprouting artificial intelligence recognition framework based on pathological sections to automatically identify the budding area to assist in the clinical diagnosis and treatment of colorectal cancer.
We retrospectively collected 100 surgical pathological sections of colorectal cancer from January 2019 to October 2019. The pathologists used LabelImg software to identify tumor buds and to count their numbers. Finally, 1,000 images were screened, and the total number of tumor buds was approximately 3,226; the images were randomly divided into a training set and a test set at a ratio of 6:4. After the images in the training set were manually identified, the identified buds in the 600 images were used to train the Faster RCNN identification model. After the establishment of the artificial intelligence identification detection platform, 400 images in the test set were used to test the identification detection system to identify and predict the area and number of tumorbuds. Finally, by comparing the results of the Faster RCNN system and the identification information of pathologists, the performance of the artificial intelligence automatic detection platform was evaluated to determine the area and number of tumor sprouting in the pathological sections of the colorectal cancers to achieve an auxiliary diagnosis and to suggest appropriate treatment. The selected performance indicators include accuracy, precision, specificity, etc. ROC (receiver operator characteristic) and AUC (area under the curve) were used to quantify the performance of the system to automatically identify tumor budding areas and numbers.
The AUC of the receiver operating characteristic curve of the artificial intelligence detection and identification system was 0.96, the image diagnosis accuracy rate was 0.89, the precision was 0.855, the sensitivity was 0.94, the specificity was 0.83, and the negative predictive value was 0.933. After 400 test sets, pathological image verification showed that there were 356 images with the same positive budding area count, and the difference between the positive area count and the manual detection count in the remaining images was less than 3. The detection system based on tumor budding recognition in pathological sections is comparable to that of pathologists’ accuracy; however, it took significantly less time (0.03±0.01)s for the pathologist (13±5)s to diagnose the sections with the assistance of the AI model.
This system can accurately and quickly identify the tumor sprouting area in the pathological sections of colorectal cancer and count their numbers, which greatly improves the diagnostic efficacy, and effectively avoids the need for confirmation by different pathologists. The use of the AI reduces the burden of pathologists in reading sections and it has a certain clinical diagnosis and treatment value.
This study aimed to summarize the characteristics and methodological quality of systematic reviews on the application of artificial intelligence (AI) in clinical diagnosis and treatment.
We systematically searched seven English- and Chinese-language literature databases to identify systematic reviews on the application of AI, deep learning, or machine learning in the diagnosis and treatment of any disease published in 2020. We evaluated the methodological quality of the included systematic reviews using "A Measurement tool for the assessment of multiple systematic reviews" (AMSTAR). We also conducted meta-analyses on the diagnostic accuracy of AI on selected disease categories with a large number of included studies and low clinical heterogeneity.
A total of 40 systematic reviews reporting 1,083 original studies were included, covering 31 diseases from 11 groups of diseases. Eleven systematic reviews were related to neoplasms and nine were systematic reviews related to diseases of the digestive system. We selected digestive system diseases for the meta-analysis. The pooled sensitivities (with 95% confidence interval (CI)) of AI to assist the diagnosis of helicobacter pylori, gastrointestinal ulcers, hemorrhage, esophageal tumors, gastric tumors, and intestinal tumors (with 95% CI) were 0.91 (0.83-0.95), 0.99 (0.76-1.00), 0.95 (0.83-0.99), 0.90 (0.85-0.93), 0.90 (0.82-0.95), and 0.93 (0.88-0.96), respectively, and the pooled specificities were 0.82 (0.77-0.87), 0.97 (0.86-1.00), 1.00 (0.99-1.00), 0.80 (0.71-0.87), 0.93 (0.87-0.97), and 0.89 (0.85-0.92), respectively. The AMSTAR items "the list of included studies" (n = 39, 97.5%) and "the characteristics of the included studies" (n = 39, 97.5%) had the highest compliance among the reviews; the compliance was relatively low to the items "the consideration of publication status" (n = 1, 2.5%), "the consideration of scientific quality" (n = 19, 47.5%), "data synthesis methods" (n = 18, 45.0%), and " the evaluation of publication bias" (n = 13, 32.5%).
The main subjects of systematic reviews on AI applications in clinical diagnosis and treatment published in 2020 were diseases of the digestive system and neoplasms. The methodological quality of the systematic reviews on AI needs to be improved, paying particular attention to publication bias and the rigorous evaluation of the quality of the included studies.
Testicular two-dimensional ultrasound is a testing modality that is often used to evaluate azoospermia and other related diseases. With the continuous development of deep learning in recent years, the combination of deep learning and testicular ultrasound appears unstoppable despite a lack of relevant standards. One of the major problems associated with the digitization of ultrasound images is the uneven quality of data however, and a standardized data source and acquisition process has not yet been developed. Such a standard could fill the current gap, and establish acquisition criteria for ultrasound images of testes during the male reproductive period, including grayscale ultrasound, shear wave elastography, and contrast-enhanced ultrasound. By following these guidelines the quality of testicular ultrasound images would be improved and standardized, which would lay a solid foundation for the standardization of testicular ultrasound images, and assist automated evaluation of testicular spermatogenic function of whole testis in azoospermic males.
CURRENT ISSUE
