What Is Machine Learning, Artificial Neural Networks and Deep Learning?—Examples of Practical Applications in Medicine

Machine learning (ML), artificial neural networks (ANNs), and deep learning (DL) are all topics that fall under the heading of artificial intelligence (AI) and have gained popularity in recent years. ML involves the application of algorithms to automate decision-making processes using models that have not been manually programmed but have been trained on data. ANNs that are a part of ML aim to simulate the structure and function of the human brain. DL, on the other hand, uses multiple layers of interconnected neurons. This enables the processing and analysis of large and complex databases. In medicine, these techniques are being introduced to improve the speed and efficiency of disease diagnosis and treatment. Each of the AI techniques presented in the paper is supported with an example of a possible medical application. Given the rapid development of technology, the use of AI in medicine shows promising results in the context of patient care. It is particularly important to keep a close eye on this issue and conduct further research in order to fully explore the potential of ML, ANNs, and DL, and bring further applications into clinical use in the future.

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References

93

[1]

Ward "Surgical Data Science and Artificial Intelligence for Surgical Education" J. Surg. Oncol. (2021) 10.1002/jso.26496

[2]

Gefter "Automated Detection and Quantification of COVID-19 Airspace Disease on Chest Radiographs: A Novel Approach Achieving Expert Radiologist-Level Performance Using a Deep Convolutional Neural Network Trained on Digital Reconstructed Radiographs from Computed Tomography-Derived Ground Truth" Investig. Radiol. (2021) 10.1097/rli.0000000000000763

[3]

Gupta, M. (2023, May 01). Introduction to Data in Machine Learning. GeeksforGeeks. Available online: https://www.geeksforgeeks.org/ml-introduction-data-machine-learning/.

[4]

Dorfman, E. (2023, May 07). How Much Data Is Required for Machine Learning? Postindustria. Available online: https://postindustria.com/how-much-data-is-required-for-machine-learning/.

[5]

Patel, H. (2023, May 01). Data-Centric Approach vs. Model-Centric Approach in Machine Learning. MLOps Blog 2023. Available online: https://neptune.ai/blog/data-centric-vs-model-centric-machine-learning.

[6]

Brown, S. (2023, May 04). Machine Learning, Explained. MIT Sloan. Ideas Made to Matter. Available online: https://mitsloan.mit.edu/ideas-made-to-matter/machine-learning-explained.

[7]

Christopher, A. (2023, May 10). K-Nearest Neighbor. Medium. The Startup. Available online: https://medium.com/swlh/k-nearest-neighbor-ca2593d7a3c4.

[8]

Hamed "Accurate Classification of COVID-19 Based on Incomplete Heterogeneous Data Using a KNN Variant Algorithm" Arab J. Sci. Eng. (2021) 10.1007/s13369-020-05212-z

[9]

Bellino, G., Schiaffino, L., Battisti, M., Guerrero, J., and Rosado-Muñoz, A. (2019). Optimization of the KNN Supervised Classification Algorithm as a Support Tool for the Implantation of Deep Brain Stimulators in Patients with Parkinson’s Disease. Entropy, 21. 10.3390/e21040346

[10]

(2023, May 11). What Is Linear Regression? IBM. Available online: https://www.ibm.com/topics/linear-regression.

[11]

Garcia, J.M.V., Bahloul, M.A., and Laleg-Kirati, T.-M. (2022, January 11–15). A Multiple Linear Regression Model for Carotid-to-Femoral Pulse Wave Velocity Estimation Based on Schrodinger Spectrum Characterization. Proceedings of the 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Glasgow, UK. 10.1109/embc48229.2022.9871031

[12]

(2023, May 11). Co to Jest Uczenie Maszynowe? Microsoft Azure. Available online: https://azure.microsoft.com/pl-pl/resources/cloud-computing-dictionary/what-is-machine-learning-platform.

[13]

(2023, May 14). Regresja Logistyczna. IBM. Available online: https://www.ibm.com/docs/pl/spss-statistics/28.0.0?topic=regression-logistic.

[14]

Kleinbaum, D.G., and Klein, M. (2010). Statistics for Biology and Health, Springer.

[15]

Gruszczyński, M., Witkowski, B., Wiśniowski, A., Szulc, A., Owczarczuk, M., Książek, M., and Bazyl, M. (2012). Mikroekonometria. Modele i Metody Analizy Danych Indywidualnych, Wolters Kluwer Polska SA. Akademicka. Ekonomia; II.

[16]

Naiwny Klasyfikator Bayesa (2023, May 02). StatSoft Internetowy Podręcznik Statystyki. Available online: https://www.statsoft.pl/textbook/stathome_stat.html?https%3A%2F%2Fwww.statsoft.pl%2Ftextbook%2Fgo_search.html%3Fq%3D%25bayersa.

[17]

Boulicaut "Nomograms for Visualization of Naive Bayesian Classifier" Knowledge Discovery in Databases: PKDD 2004 (2004)

[18]

Minsky "Steps toward Artificial Intelligence" Proc. IRE (1961) 10.1109/jrproc.1961.287775

[19]

Zhou "Sparse SVM for Sufficient Data Reduction" IEEE Trans. Pattern Anal. Mach. Intell. (2021)

[20]

Bordes "Fast Kernel Classifiers with Online and Active Learning" J. Mach. Learn. (2005)

[21]

Support-vector networks

Corinna Cortes, Vladimir Vapnik

Machine Learning 1995 10.1007/bf00994018

[22]

What is a support vector machine?

William S Noble

Nature Biotechnology 2006 10.1038/nbt1206-1565

[23]

Winters-Hilt, S., and Merat, S. (2007). SVM Clustering. BMC Bioinform., 8. 10.1186/1471-2105-8-s7-s18

[24]

Huang "Applications of Support Vector Machine (SVM) Learning in Cancer Genomics" Cancer Genom. Proteom. (2018)

[25]

Zhang "Diagnostic Method of Diabetes Based on Support Vector Machine and Tongue Images" BioMed Res. Int. (2017)

[26]

Schapire, R.E., and Freund, Y. (2012). Boosting: Foundations and Algorithms, MIT Press. Adaptive Computation and Machine Learning Series. 10.7551/mitpress/8291.001.0001

[27]

Li "Adaptive Boosting (AdaBoost)-based Multiwavelength Spatial Frequency Domain Imaging and Characterization for Ex Vivo Human Colorectal Tissue Assessment" J. Biophotonics (2020) 10.1002/jbio.201960241

[28]

Hatwell, J., Gaber, M.M., and Atif Azad, R.M. (2020). Ada-WHIPS: Explaining AdaBoost Classification with Applications in the Health Sciences. BMC Med. Inform. Decis. Mak., 20. 10.1186/s12911-020-01201-2

[29]

Baniasadi "Two-Step Imputation and AdaBoost-Based Classification for Early Prediction of Sepsis on Imbalanced Clinical Data" Crit. Care Med. (2021) 10.1097/ccm.0000000000004705

[30]

Takemura "Discrimination of Breast Tumors in Ultrasonic Images Using an Ensemble Classifier Based on the AdaBoost Algorithm with Feature Selection" IEEE Trans. Med. Imaging (2010) 10.1109/tmi.2009.2022630

[31]

Salcedo-Sanz, S., Pérez-Aracil, J., Ascenso, G., Del Ser, J., Casillas-Pérez, D., Kadow, C., Fister, D., Barriopedro, D., García-Herrera, R., and Restelli, M. (2022). Analysis, Characterization, Prediction and Attribution of Extreme Atmospheric Events with Machine Learning: A Review. arXiv.

[32]

Moore "XGBoost, A Novel Explainable AI Technique, in the Prediction of Myocardial Infarction: A UK Biobank Cohort Study" Clin. Med. Insights Cardiol. (2022) 10.1177/11795468221133611

[33]

Wang "Predicting the Prognosis of Patients in the Coronary Care Unit: A Novel Multi-Category Machine Learning Model Using XGBoost" Front. Cardiovasc. Med. (2022) 10.3389/fcvm.2022.764629

[34]

Séroussi, B., Weber, P., Dhombres, F., Grouin, C., Liebe, J.-D., Pelayo, S., Pinna, A., Rance, B., Sacchi, L., and Ugon, A. (2022). Studies in Health Technology and Informatics, IOS Press.

[35]

Inoue "XGBoost, a Machine Learning Method, Predicts Neurological Recovery in Patients with Cervical Spinal Cord Injury" Neurotrauma Rep. (2020) 10.1089/neur.2020.0009

[36]

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017, January 4–9). Attention Is All You Need. Proceedings of the NIPS 2017, Long Beach, CA, USA.

[37]

Shao, R., Shi, Z., Yi, J., Chen, P.-Y., and Hsieh, C.-J. (2021). On the Adversarial Robustness of Vision Transformers. arXiv.

[38]

Qureshi, J. (2023, May 03). What Is the Difference between Neural Networks and Deep Neural Networks? Quora 2018. Available online: https://www.quora.com/What-is-the-difference-between-neural-networks-and-deep-neural-networks.

[39]

Jeffrey, C. (2023, May 03). Explainer: What Is Machine Learning? TechSpot 2020. Available online: https://www.techspot.com/article/2048-machine-learning-explained/.

[40]

McBee "Deep Learning in Radiology" Acad. Radiol. (2018) 10.1016/j.acra.2018.02.018

[41]

Lee "Deep Learning in Medical Image Analysis" Deep Learning in Medical Image Analysis (2020) 10.1007/978-3-030-33128-3_1

[42]

Kriegeskorte "Neural Network Models and Deep Learning" Curr. Biol. (2019) 10.1016/j.cub.2019.02.034

[43]

Bajić, F., Orel, O., and Habijan, M. (2022). A Multi-Purpose Shallow Convolutional Neural Network for Chart Images. Sensors, 22. 10.3390/s22207695

[44]

Han "Predicting Oral Disintegrating Tablet Formulations by Neural Network Techniques" Asian J. Pharm. Sci. (2018) 10.1016/j.ajps.2018.01.003

[45]

Egger "Medical Deep Learning—A Systematic Meta-Review" Comput. Methods Programs Biomed. (2022) 10.1016/j.cmpb.2022.106874

[46]

Jafari "Deep Neural Network for Water/Fat Separation: Supervised Training, Unsupervised Training, and No Training" Magn. Reson. Med. (2021) 10.1002/mrm.28546

[47]

Hou "A Deep Neural Network-Based Model for Quantitative Evaluation of the Effects of Swimming Training" Comput. Intell. Neurosci. (2022) 10.1155/2022/5508365

[48]

Singh "Automated Detection of Scaphoid Fractures Using Deep Neural Networks in Radiographs" Eng. Appl. Artif. Intell. (2023) 10.1016/j.engappai.2023.106165

[49]

Gülmez, B. A Novel Deep Neural Network Model Based Xception and Genetic Algorithm for Detection of COVID-19 from X-Ray Images. Ann. Oper. Res., 2022. 10.1007/s10479-022-05151-y

[50]

Tsai, K.-J., Chou, M.-C., Li, H.-M., Liu, S.-T., Hsu, J.-H., Yeh, W.-C., Hung, C.-M., Yeh, C.-Y., and Hwang, S.-H. (2022). A High-Performance Deep Neural Network Model for BI-RADS Classification of Screening Mammography. Sensors, 22. 10.3390/s22031160

Showing 50 of 93 references

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Details

Published: Aug 03, 2023
Vol/Issue: 13(15)
Pages: 2582
License: View

Authors

J

Jakub Kufel

Department of Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, 41-808 Zabrze, Poland

K

Katarzyna Bargieł-Łączek

Paediatric Radiology Students’ Scientific Association at the Division of Diagnostic Imaging, Department of Radiology and Nuclear Medicine, Faculty of Medical Science in Katowice, Medical University of Silesia, 40-752 Katowice, Poland

S

Szymon Kocot

Bright Coders’ Factory, Technologiczna 2, 45-839 Opole, Poland

M

Maciej Koźlik

Division of Cardiology and Structural Heart Disease, Medical University of Silesia, 40-635 Katowice, Poland

W

Wiktoria Bartnikowska

Paediatric Radiology Students’ Scientific Association at the Division of Diagnostic Imaging, Department of Radiology and Nuclear Medicine, Faculty of Medical Science in Katowice, Medical University of Silesia, 40-752 Katowice, Poland

M

Michał Janik