scholarly journals mSphere of Influence: the Rise of Artificial Intelligence in Infection Biology

mSphere ◽  
2019 ◽  
Vol 4 (3) ◽  
Author(s):  
Artur Yakimovich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Label Free ◽  
Edge Label ◽  
Bacterial Colony ◽  
Link Type ◽  
Colony Counting ◽  
Anthrax Spores ◽  
Infection Biology

ABSTRACT Artur Yakimovich works in the field of computational virology and applies machine learning algorithms to study host-pathogen interactions. In this mSphere of Influence article, he reflects on two papers “Holographic Deep Learning for Rapid Optical Screening of Anthrax Spores” by Jo et al. (Y. Jo, S. Park, J. Jung, J. Yoon, et al., Sci Adv 3:e1700606, 2017, https://doi.org/10.1126/sciadv.1700606) and “Bacterial Colony Counting with Convolutional Neural Networks in Digital Microbiology Imaging” by Ferrari and colleagues (A. Ferrari, S. Lombardi, and A. Signoroni, Pattern Recognition 61:629–640, 2017, https://doi.org/10.1016/j.patcog.2016.07.016). Here he discusses how these papers made an impact on him by showcasing that artificial intelligence algorithms can be equally applicable to both classical infection biology techniques and cutting-edge label-free imaging of pathogens.

Development of innovative hygienic technologies for preservation of the workers’ reproductive health

2020 ◽  
pp. 792-792
Author(s):  
M. A. Fesenko ◽  
G. V. Golovaneva ◽  
A. V. Miskevich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Reproductive Health ◽  
Reproductive System ◽  
Preventive Measures ◽  
Learning Algorithms ◽  
Reproductive Function ◽  
Machine Learning Algorithms ◽  
New Model

The new model «Prognosis of men’ reproductive function disorders» was developed. The machine learning algorithms (artificial intelligence) was used for this purpose, the model has high prognosis accuracy. The aim of the model applying is prioritize diagnostic and preventive measures to minimize reproductive system diseases complications and preserve workers’ health and efficiency.

Artificial Intelligence, Machine Learning and Calculation of Intraocular Lens Power

2020 ◽  
Vol 237 (12) ◽  
pp. 1430-1437
Author(s):  
Achim Langenbucher ◽  
Nóra Szentmáry ◽  
Jascha Wendelstein ◽  
Peter Hoffmann
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Regression Model ◽  
Prediction Error ◽  
Learning Algorithms ◽  
Anterior Chamber Depth ◽  
Gaussian Process Regression ◽  
Machine Learning Algorithms ◽  
Effect Sizes ◽  
Axial Position

Abstract Background and Purpose In the last decade, artificial intelligence and machine learning algorithms have been more and more established for the screening and detection of diseases and pathologies, as well as for describing interactions between measures where classical methods are too complex or fail. The purpose of this paper is to model the measured postoperative position of an intraocular lens implant after cataract surgery, based on preoperatively assessed biometric effect sizes using techniques of machine learning. Patients and Methods In this study, we enrolled 249 eyes of patients who underwent elective cataract surgery at Augenklinik Castrop-Rauxel. Eyes were measured preoperatively with the IOLMaster 700 (Carl Zeiss Meditec), as well as preoperatively and postoperatively with the Casia 2 OCT (Tomey). Based on preoperative effect sizes axial length, corneal thickness, internal anterior chamber depth, thickness of the crystalline lens, mean corneal radius and corneal diameter a selection of 17 machine learning algorithms were tested for prediction performance for calculation of internal anterior chamber depth (AQD_post) and axial position of equatorial plane of the lens in the pseudophakic eye (LEQ_post). Results The 17 machine learning algorithms (out of 4 families) varied in root mean squared/mean absolute prediction error between 0.187/0.139 mm and 0.255/0.204 mm (AQD_post) and 0.183/0.135 mm and 0.253/0.206 mm (LEQ_post), using 5-fold cross validation techniques. The Gaussian Process Regression Model using an exponential kernel showed the best performance in terms of root mean squared error for prediction of AQDpost and LEQpost. If the entire dataset is used (without splitting for training and validation data), comparison of a simple multivariate linear regression model vs. the algorithm with the best performance showed a root mean squared prediction error for AQD_post/LEQ_post with 0.188/0.187 mm vs. the best performance Gaussian Process Regression Model with 0.166/0.159 mm. Conclusion In this paper we wanted to show the principles of supervised machine learning applied to prediction of the measured physical postoperative axial position of the intraocular lenses. Based on our limited data pool and the algorithms used in our setting, the benefit of machine learning algorithms seems to be limited compared to a standard multivariate regression model.

scholarly journals Reviewing the relationship between machines and radiology: the application of artificial intelligence

2021 ◽  
Vol 10 (2) ◽  
pp. 205846012199029
Author(s):  
Rani Ahmad
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Health Care Professionals ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Health Science ◽  
Computer Algorithms ◽  
Learning Models ◽  
Specificity And Sensitivity

Background The scope and productivity of artificial intelligence applications in health science and medicine, particularly in medical imaging, are rapidly progressing, with relatively recent developments in big data and deep learning and increasingly powerful computer algorithms. Accordingly, there are a number of opportunities and challenges for the radiological community. Purpose To provide review on the challenges and barriers experienced in diagnostic radiology on the basis of the key clinical applications of machine learning techniques. Material and Methods Studies published in 2010–2019 were selected that report on the efficacy of machine learning models. A single contingency table was selected for each study to report the highest accuracy of radiology professionals and machine learning algorithms, and a meta-analysis of studies was conducted based on contingency tables. Results The specificity for all the deep learning models ranged from 39% to 100%, whereas sensitivity ranged from 85% to 100%. The pooled sensitivity and specificity were 89% and 85% for the deep learning algorithms for detecting abnormalities compared to 75% and 91% for radiology experts, respectively. The pooled specificity and sensitivity for comparison between radiology professionals and deep learning algorithms were 91% and 81% for deep learning models and 85% and 73% for radiology professionals (p < 0.000), respectively. The pooled sensitivity detection was 82% for health-care professionals and 83% for deep learning algorithms (p < 0.005). Conclusion Radiomic information extracted through machine learning programs form images that may not be discernible through visual examination, thus may improve the prognostic and diagnostic value of data sets.

scholarly journals Schizophrenia: A Survey of Artificial Intelligence Techniques Applied to Detection and Classification

2021 ◽  
Vol 18 (11) ◽  
pp. 6099
Author(s):  
Joel Weijia Lai ◽  
Candice Ke En Ang ◽  
U. Rajendra Acharya ◽  
Kang Hao Cheong
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Daily Living ◽  
Learning Algorithms ◽  
Health Condition ◽  
Machine Learning Algorithms ◽  
Human Cognition ◽  
Computer Algorithms ◽  
Large Sets ◽  
Prevention Methods

Artificial Intelligence in healthcare employs machine learning algorithms to emulate human cognition in the analysis of complicated or large sets of data. Specifically, artificial intelligence taps on the ability of computer algorithms and software with allowable thresholds to make deterministic approximate conclusions. In comparison to traditional technologies in healthcare, artificial intelligence enhances the process of data analysis without the need for human input, producing nearly equally reliable, well defined output. Schizophrenia is a chronic mental health condition that affects millions worldwide, with impairment in thinking and behaviour that may be significantly disabling to daily living. Multiple artificial intelligence and machine learning algorithms have been utilized to analyze the different components of schizophrenia, such as in prediction of disease, and assessment of current prevention methods. These are carried out in hope of assisting with diagnosis and provision of viable options for individuals affected. In this paper, we review the progress of the use of artificial intelligence in schizophrenia.

Machine Learning-Assisted Sampling of SERS Substrates Improves Data Collection Efficiency

2021 ◽  
pp. 000370282110345
Author(s):  
Tatu Rojalin ◽  
Dexter Antonio ◽  
Ambarish Kulkarni ◽  
Randy P. Carney
Keyword(s):  
Machine Learning ◽  
Data Collection ◽  
Domain Knowledge ◽  
Collection Efficiency ◽  
Point Of Care ◽  
Automated Analysis ◽  
Downstream Processing ◽  
Machine Learning Algorithms ◽  
Label Free ◽  
Expert User

Surface-enhanced Raman scattering (SERS) is a powerful technique for sensitive label-free analysis of chemical and biological samples. While much recent work has established sophisticated automation routines using machine learning and related artificial intelligence methods, these efforts have largely focused on downstream processing (e.g., classification tasks) of previously collected data. While fully automated analysis pipelines are desirable, current progress is limited by cumbersome and manually intensive sample preparation and data collection steps. Specifically, a typical lab-scale SERS experiment requires the user to evaluate the quality and reliability of the measurement (i.e., the spectra) as the data are being collected. This need for expert user-intuition is a major bottleneck that limits applicability of SERS-based diagnostics for point-of-care clinical applications, where trained spectroscopists are likely unavailable. While application-agnostic numerical approaches (e.g., signal-to-noise thresholding) are useful, there is an urgent need to develop algorithms that leverage expert user intuition and domain knowledge to simplify and accelerate data collection steps. To address this challenge, in this work, we introduce a machine learning-assisted method at the acquisition stage. We tested six common algorithms to measure best performance in the context of spectral quality judgment. For adoption into future automation platforms, we developed an open-source python package tailored for rapid expert user annotation to train machine learning algorithms. We expect that this new approach to use machine learning to assist in data acquisition can serve as a useful building block for point-of-care SERS diagnostic platforms.

AI-Enabled Support System for Melanoma Detection and Classification

2021 ◽  
Vol 10 (4) ◽  
pp. 58-75
Author(s):  
Vivek Sen Saxena ◽  
Prashant Johri ◽  
Avneesh Kumar
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Skin Lesion ◽  
Performance Metrics ◽  
Similarity Index ◽  
Skin Lesions ◽  
Machine Learning Algorithms ◽  
Lesion Area ◽  
Melanoma Detection ◽  
Grabcut Algorithm

Skin lesion melanoma is the deadliest type of cancer. Artificial intelligence provides the power to classify skin lesions as melanoma and non-melanoma. The proposed system for melanoma detection and classification involves four steps: pre-processing, resizing all the images, removing noise and hair from dermoscopic images; image segmentation, identifying the lesion area; feature extraction, extracting features from segmented lesion and classification; and categorizing lesion as malignant (melanoma) and benign (non-melanoma). Modified GrabCut algorithm is employed to generate skin lesion. Segmented lesions are classified using machine learning algorithms such as SVM, k-NN, ANN, and logistic regression and evaluated on performance metrics like accuracy, sensitivity, and specificity. Results are compared with existing systems and achieved higher similarity index and accuracy.

scholarly journals AI for augmenting human judgement in Business Processes Management

2021 ◽  
Vol 201 (3) ◽  
pp. 507-518
Author(s):  
Łukasz Osuszek ◽  
Stanisław Stanek
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Support ◽  
Business Processes ◽  
Workflow Management ◽  
Organizational Memory ◽  
Machine Learning Algorithms ◽  
Analytical Models ◽  
Case Based Reasoning ◽  
Historical Case

The paper outlines the recent trends in the evolution of Business Process Management (BPM) – especially the application of AI for decision support. AI has great potential to augment human judgement. Indeed, Machine Learning might be considered as a supplementary and complimentary solution to enhance and support human productivity throughout all aspects of personal and professional life. The idea of merging technologies for organizational learning and workflow management was first put forward by Wargitsch. Herein, completed business cases stored in an organizational memory are used to configure new workflows, while the selection of an appropriate historical case is supported by a case-based reasoning component. This informational environment has been recognized in the world as being effective and has become quite common because of the significant increase in the use of artificial intelligence tools. This article discusses also how automated planning techniques (one of the oldest areas in AI) can be used to enable a new level of automation and processing support. The authors of the article decided to analyse this topic and discuss the scientific state of the art and the application of AI in BPM systems for decision-making support. It should be noted that readily available software exists for the needs of the development of such systems in the field of artificial intelligence. The paper also includes a unique case study with production system of Decision Support, using controlled machine learning algorithms to predictive analytical models.

scholarly journals An analytical survey on the role of machine learning algorithms in case of intrusion detection

2020 ◽  
Vol 5 (19) ◽  
pp. 32-35
Author(s):  
Anand Vijay ◽  
Kailash Patidar ◽  
Manoj Yadav ◽  
Rishi Kushwah
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Handling Mechanism ◽  
The Impact

In this paper an analytical survey on the role of machine learning algorithms in case of intrusion detection has been presented and discussed. This paper shows the analytical aspects in the development of efficient intrusion detection system (IDS). The related study for the development of this system has been presented in terms of computational methods. The discussed methods are data mining, artificial intelligence and machine learning. It has been discussed along with the attack parameters and attack types. This paper also elaborates the impact of different attack and handling mechanism based on the previous papers.

scholarly journals A Bayesian neural network predicts the dissolution of compact planetary systems

2021 ◽  
Vol 118 (40) ◽  
pp. e2026053118
Author(s):  
Miles Cranmer ◽  
Daniel Tamayo ◽  
Hanno Rein ◽  
Peter Battaglia ◽  
Samuel Hadden ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Time Series ◽  
Planetary System ◽  
Machine Learning Algorithms ◽  
Orbital Elements ◽  
Bayesian Neural Network ◽  
Inference Model ◽  
Link Type ◽  
Numerical Integrator

We introduce a Bayesian neural network model that can accurately predict not only if, but also when a compact planetary system with three or more planets will go unstable. Our model, trained directly from short N-body time series of raw orbital elements, is more than two orders of magnitude more accurate at predicting instability times than analytical estimators, while also reducing the bias of existing machine learning algorithms by nearly a factor of three. Despite being trained on compact resonant and near-resonant three-planet configurations, the model demonstrates robust generalization to both nonresonant and higher multiplicity configurations, in the latter case outperforming models fit to that specific set of integrations. The model computes instability estimates up to 105 times faster than a numerical integrator, and unlike previous efforts provides confidence intervals on its predictions. Our inference model is publicly available in the SPOCK (https://github.com/dtamayo/spock) package, with training code open sourced (https://github.com/MilesCranmer/bnn_chaos_model).

scholarly journals Combining Regression and Clustering for Financial Analysis

2020 ◽  
Vol 2 (1) ◽  
pp. 109-118
Author(s):  
Andreea Ioana Chiriac
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cluster Analysis ◽  
Business Processes ◽  
Financial Analysis ◽  
Two Dimensions ◽  
Machine Learning Algorithms ◽  
Financial Ratio ◽  
Research Perspective ◽  
Systems Learning

Abstract Artificial Intelligence is used in business through machine learning algorithms. Machine learning is a part of computer science focused on computer systems learning to perform a specific task without using explicit instructions, relying on patterns and inference instead. Though it might seem like we’ve come a long way in the last ten years, which is true from a research perspective, the adoption of AI among corporations is still relatively low. Over time it became possible to automate more tasks and business processes than ever before. The benefit of using artificial intelligence is that does not require to program every step of the process, predicting at each step what could happen and how to resolve it. The algorithms decide for themselves in each case how the problems should be solved, based on the data that is used. I apply Python language to create a synthetic feature vector that allows visualizations in two dimensions for EDIBTA financial ratio. I use Mean-Square Error in order to evaluate the success, having the optimal parameters. In this section, I also mentioned about the purpose, goals, and applications of cluster analysis. I indicated about the basics of cluster analysis and how to do it and also did a demonstration on how to use K-Means.

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