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Journal of Oral Pathology & Medicine :... Oct 2020Recently, there has been a momentous drive to apply advanced artificial intelligence (AI) technologies to diagnostic medicine. The introduction of AI has provided vast... (Review)
Review
BACKGROUND
Recently, there has been a momentous drive to apply advanced artificial intelligence (AI) technologies to diagnostic medicine. The introduction of AI has provided vast new opportunities to improve health care and has introduced a new wave of heightened precision in oncologic pathology. The impact of AI on oncologic pathology has now become apparent, and its use with respect to oral oncology is still in the nascent stage.
DISCUSSION
A foundational overview of AI classification systems used in medicine and a review of common terminology used in machine learning and computational pathology will be presented. This paper provides a focused review on the recent advances in AI and deep learning in oncologic histopathology and oral oncology. In addition, specific emphasis on recent studies that have applied these technologies to oral cancer prognostication will also be discussed.
CONCLUSION
Machine and deep learning methods designed to enhance prognostication of oral cancer have been proposed with much of the work focused on prediction models on patient survival and locoregional recurrences in patients with oral squamous cell carcinomas (OSCC). Few studies have explored machine learning methods on OSCC digital histopathologic images. It is evident that further research at the whole slide image level is needed and future collaborations with computer scientists may progress the field of oral oncology.
Topics: Artificial Intelligence; Deep Learning; Humans; Machine Learning; Neoplasm Recurrence, Local
PubMed: 32449232
DOI: 10.1111/jop.13042 -
Nature Reviews. Genetics Jul 2019As a data-driven science, genomics largely utilizes machine learning to capture dependencies in data and derive novel biological hypotheses. However, the ability to... (Review)
Review
As a data-driven science, genomics largely utilizes machine learning to capture dependencies in data and derive novel biological hypotheses. However, the ability to extract new insights from the exponentially increasing volume of genomics data requires more expressive machine learning models. By effectively leveraging large data sets, deep learning has transformed fields such as computer vision and natural language processing. Now, it is becoming the method of choice for many genomics modelling tasks, including predicting the impact of genetic variation on gene regulatory mechanisms such as DNA accessibility and splicing.
Topics: Base Sequence; Computer Simulation; Deep Learning; Genomics; Humans; Models, Genetic; Neural Networks, Computer; Supervised Machine Learning; Unsupervised Machine Learning
PubMed: 30971806
DOI: 10.1038/s41576-019-0122-6 -
Clinical Microbiology and Infection :... May 2020Machine learning (ML) is a growing field in medicine. This narrative review describes the current body of literature on ML for clinical decision support in infectious... (Review)
Review
BACKGROUND
Machine learning (ML) is a growing field in medicine. This narrative review describes the current body of literature on ML for clinical decision support in infectious diseases (ID).
OBJECTIVES
We aim to inform clinicians about the use of ML for diagnosis, classification, outcome prediction and antimicrobial management in ID.
SOURCES
References for this review were identified through searches of MEDLINE/PubMed, EMBASE, Google Scholar, biorXiv, ACM Digital Library, arXiV and IEEE Xplore Digital Library up to July 2019.
CONTENT
We found 60 unique ML-clinical decision support systems (ML-CDSS) aiming to assist ID clinicians. Overall, 37 (62%) focused on bacterial infections, 10 (17%) on viral infections, nine (15%) on tuberculosis and four (7%) on any kind of infection. Among them, 20 (33%) addressed the diagnosis of infection, 18 (30%) the prediction, early detection or stratification of sepsis, 13 (22%) the prediction of treatment response, four (7%) the prediction of antibiotic resistance, three (5%) the choice of antibiotic regimen and two (3%) the choice of a combination antiretroviral therapy. The ML-CDSS were developed for intensive care units (n = 24, 40%), ID consultation (n = 15, 25%), medical or surgical wards (n = 13, 20%), emergency department (n = 4, 7%), primary care (n = 3, 5%) and antimicrobial stewardship (n = 1, 2%). Fifty-three ML-CDSS (88%) were developed using data from high-income countries and seven (12%) with data from low- and middle-income countries (LMIC). The evaluation of ML-CDSS was limited to measures of performance (e.g. sensitivity, specificity) for 57 ML-CDSS (95%) and included data in clinical practice for three (5%).
IMPLICATIONS
Considering comprehensive patient data from socioeconomically diverse healthcare settings, including primary care and LMICs, may improve the ability of ML-CDSS to suggest decisions adapted to various clinical contexts. Currents gaps identified in the evaluation of ML-CDSS must also be addressed in order to know the potential impact of such tools for clinicians and patients.
Topics: Anti-Infective Agents; Artificial Intelligence; Clinical Decision-Making; Communicable Diseases; Decision Support Systems, Clinical; Early Diagnosis; Humans; Machine Learning; Patient Outcome Assessment; Sepsis
PubMed: 31539636
DOI: 10.1016/j.cmi.2019.09.009 -
Bioinformatics (Oxford, England) Oct 2019In a predictive modeling setting, if sufficient details of the system behavior are known, one can build and use a simulation for making predictions. When sufficient...
MOTIVATION
In a predictive modeling setting, if sufficient details of the system behavior are known, one can build and use a simulation for making predictions. When sufficient system details are not known, one typically turns to machine learning, which builds a black-box model of the system using a large dataset of input sample features and outputs. We consider a setting which is between these two extremes: some details of the system mechanics are known but not enough for creating simulations that can be used to make high quality predictions. In this context we propose using approximate simulations to build a kernel for use in kernelized machine learning methods, such as support vector machines. The results of multiple simulations (under various uncertainty scenarios) are used to compute similarity measures between every pair of samples: sample pairs are given a high similarity score if they behave similarly under a wide range of simulation parameters. These similarity values, rather than the original high dimensional feature data, are used to build the kernel.
RESULTS
We demonstrate and explore the simulation-based kernel (SimKern) concept using four synthetic complex systems-three biologically inspired models and one network flow optimization model. We show that, when the number of training samples is small compared to the number of features, the SimKern approach dominates over no-prior-knowledge methods. This approach should be applicable in all disciplines where predictive models are sought and informative yet approximate simulations are available.
AVAILABILITY AND IMPLEMENTATION
The Python SimKern software, the demonstration models (in MATLAB, R), and the datasets are available at https://github.com/davidcraft/SimKern.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
Topics: Machine Learning; Software; Support Vector Machine
PubMed: 30903692
DOI: 10.1093/bioinformatics/btz199 -
Methods (San Diego, Calif.) Jan 2023
Topics: Deep Learning; Computational Biology; Machine Learning
PubMed: 36503039
DOI: 10.1016/j.ymeth.2022.12.001 -
TheScientificWorldJournal 2015
Topics: Machine Learning; Medical Informatics
PubMed: 25692180
DOI: 10.1155/2015/825267 -
Ultraschall in Der Medizin (Stuttgart,... Aug 2018
Topics: Deep Learning; Machine Learning; Ultrasonography
PubMed: 30071556
DOI: 10.1055/a-0642-9545 -
Medical Physics Jun 2023
Topics: Deep Learning; Diagnostic Imaging; Machine Learning; Radiography; Computers
PubMed: 36416869
DOI: 10.1002/mp.16025 -
ACS Sensors Nov 2020Chemometrics play a critical role in biosensors-based detection, analysis, and diagnosis. Nowadays, as a branch of artificial intelligence (AI), machine learning (ML)... (Review)
Review
Chemometrics play a critical role in biosensors-based detection, analysis, and diagnosis. Nowadays, as a branch of artificial intelligence (AI), machine learning (ML) have achieved impressive advances. However, novel advanced ML methods, especially deep learning, which is famous for image analysis, facial recognition, and speech recognition, has remained relatively elusive to the biosensor community. Herein, how ML can be beneficial to biosensors is systematically discussed. The advantages and drawbacks of most popular ML algorithms are summarized on the basis of sensing data analysis. Specially, deep learning methods such as convolutional neural network (CNN) and recurrent neural network (RNN) are emphasized. Diverse ML-assisted electrochemical biosensors, wearable electronics, SERS and other spectra-based biosensors, fluorescence biosensors and colorimetric biosensors are comprehensively discussed. Furthermore, biosensor networks and multibiosensor data fusion are introduced. This review will nicely bridge ML with biosensors, and greatly expand chemometrics for detection, analysis, and diagnosis.
Topics: Artificial Intelligence; Biosensing Techniques; Deep Learning; Machine Learning; Neural Networks, Computer
PubMed: 33185417
DOI: 10.1021/acssensors.0c01424 -
NMR in Biomedicine Apr 2022
Topics: Deep Learning; Machine Learning; Magnetic Resonance Imaging; Magnetic Resonance Spectroscopy
PubMed: 35253294
DOI: 10.1002/nbm.4713