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Revolutionizing Healthcare: How Deep Learning is poised to Change the Landscape of Medical Diagnosis and Treatment

Authors

  • Ahsan Ahmad Depaul University. 1 E Jackson Blvd, Chicago, IL 60604, USA
  • Aftab Tariq American National University 1814 E Main St Salem VA 24153
  • Hafiz Khawar Hussain Depaul University. 1 E Jackson Blvd, Chicago, IL 60604, USA
  • Ahmad Yousaf Gill American National University 1814 E Main St Salem VA 24153

DOI:

10.47709/cnahpc.v5i2.2350

Keywords:

Deep learning, Healthcare, Personalized medicine, Ethical considerations, Future directions, Case studies, Data privacy and security, Patient outcomes.

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Abstract

Deep learning has become a significant tool in the healthcare industry with the potential to change the way care is provided and enhance patient outcomes. With a focus on personalised medicine, ethical issues and problems, future directions and opportunities, real-world case studies, and data privacy and security, this review article investigates the existing and potential applications of deep learning in healthcare. Deep learning in personalised medicine holds enormous promise for improving patient care by enabling more precise diagnoses and individualised treatment approaches. But it's important to take into account ethical issues like data privacy and the possibility of bias in algorithms. Deep learning in healthcare will likely be used more in the future to manage population health, prevent disease, and improve access to care for underprivileged groups of people. Case studies give specific examples of how deep learning is already changing the healthcare industry, from discovering rare diseases to forecasting patient outcomes. To fully realize the potential of deep learning in healthcare, however, issues including data quality, interpretability, and legal barriers must be resolved. Remote monitoring and telemedicine are two promising areas where deep learning is lowering healthcare expenses and enhancing access to care. Deep learning algorithms can be used to analyse patient data in real-time, warning medical professionals of possible problems before they worsen and allowing for online discussions with experts. Finally, when applying deep learning to healthcare, the importance of data security and privacy cannot be understated. To preserve patient data and guarantee its responsible usage, the appropriate safeguards and rules must be implemented. Deep learning has the ability to transform the healthcare industry by delivering more individualised, practical, and efficient care. However, in order to fully realize its promise, ethical issues, difficulties, and regulatory barriers must be solved. Deep learning has the potential to significantly contribute to enhancing patient outcomes and lowering healthcare costs with the right safeguards and ongoing innovation

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References

H. Kantz, J. Kurths, G. Mayer-Kress, Nonlinear analysis of physiological data, SpringerScience & Business Media, 2012.

L. S¨ornmo, P. Laguna, Bioelectrical signal processing in cardiac and neurological applications, volume 8, Academic Press, 2005.

S. R. Devasahayam, Signals and systems in biomedical engineering: signal processing andphysiological systems modeling, Springer Science & Business Media, 2012.

K. G¨odel, on formally undecidable propositions of Principia Mathematica and relatedsystems, Courier Corporation, 1992.19

L. K. Morrell, F. Morrell, Evoked potentials and reaction times: a study of intra-individualvariability, Electroencephalography and Clinical Neurophysiology 20 (1966) 567–575.

B. Schijvenaars, Intra-individual Variability of the Electrocardiogram: Assessment andexploitation in computerized ECG analysis, 2000.

Yeruva, A. R. (2023). Providing A Personalized Healthcare Service To The Patients Using AIOPs Monitoring. Eduvest-Journal of Universal Studies, 3(2), 327-334.

T. Vertinsky, B. Forster, Prevalence of eye strain among radiologists: influence of viewingvariables on symptoms, American Journal of Roentgenology 184 (2005) 681–686.

U. R. Acharya, O. Faust, S. V. Sree, D. N. Ghista, S. Dua, P. Joseph, V. T. Ahamed,N. Janarthanan, T. Tamura, An integrated diabetic index using heart rate variability signalfeatures for diagnosis of diabetes, Computer methods in biomechanics and biomedical engineering 16 (2013) 222–234

O. Faust, U. R. Acharya, T. Tamura, Formal design methods for reliable computer-aideddiagnosis: a review, IEEE reviews in biomedical engineering 5 (2012) 15–28.

K. Y. Zhi, O. Faust, W. Yu, Wavelet based machine learning techniques for electrocardiogram signal analysis, Journal of Medical Imaging and Health Informatics 4 (2014)737–742.

O. Faust, U. R. Acharya, E. Ng, H. Fujita, A review of ecg-based diagnosis supportsystems for obstructive sleep apnea, Journal of Mechanics in Medicine and Biology 16(2016) 1640004.

O. Faust, U. R. Acharya, V. K. Sudarshan, R. San Tan, C. H. Yeong, F. Molinari, K. H. Ng,Computer aided diagnosis of coronary artery disease, myocardial infarction and carotid atherosclerosis using ultrasound images: A review, Physica Medica 33 (2017) 1–15.

R. Rao, R. Derakhshani, A comparison of eeg preprocessing methods using time delay Neural networks, in: Neural Engineering, 2005. Conference Proceedings. 2nd International IEEE EMBS Conference on, IEEE, pp. 262–264.

T. Kalayci, O. Ozdamar, Wavelet preprocessing for automated neural network detectionof eeg spikes, IEEE engineering in medicine and biology magazine 14 (1995) 160–166.20

O. Faust, U. R. Acharya, E. Ng, T. J. Hong, W. Yu, Application of infrared thermographyin computer aided diagnosis, Infrared Physics & Technology 66 (2014) 160–175.

H. Yoon, K. Yang, C. Shahabi, Feature subset selection and feature ranking for multivariate time series, IEEE transactions on knowledge and data engineering 17 (2005)1186–1198.

H. Liu, H. Motoda, Computational methods of feature selection, CRC Press, 2007.

Y. LeCun, Y. Bengio, G. Hinton, Deep learning, Nature 521 (2015) 436–444.

N. Z. N. Jenny, O. Faust, W. Yu, Automated classification of normal and prematureventricular contractions in electrocardiogram signals, Journal of Medical Imaging andHealth Informatics 4 (2014) 886–892.

O. Faust, L. M. Yi, L. M. Hua, Heart rate variability analysis for different age and gender,Journal of Medical Imaging and Health Informatics 3 (2013) 395–400.

P. D. McAndrew, D. L. Potash, B. Higgins, J. Wayand, J. Held, Expert system for providing interactive assistance in solving problems such as health care management, 1996.US Patent 5,517,405.

L. Squire, D. Berg, F. E. Bloom, S. Du Lac, A. Ghosh, N. C. Spitzer, Fundamental Neuroscience, Academic Press, 2012.

Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document Recognition, Proceedings of the IEEE 86 (1998) 2278–2324.

Y. LeCun, Y. Bengio, et al., Convolutional networks for images, speech, and time series,The handbook of brain theory and neural networks 3361 (1995) 1995.

G. E. Hinton, S. Osindero, Y.-W. Teh, A fast learning algorithm for deep belief nets,Neural computation 18 (2006) 1527–1554.

J. Hopfield, Neural networks and physical systems with emergent collective computational abilities, in: Spin Glass Theory and Beyond: An Introduction to the Replica Method and Its Applications, World Scientific, 1987, pp. 411–415.

H. Larochelle, M. Mandel, R. Pascanu, Y. Bengio, Learning algorithms for the classificationRestricted boltzmann machine, Journal of Machine Learning Research 13 (2012) 643–669.

R. Salakhutdinov, Learning deep generative models, University of Toronto, 2009.

Y. Bengio, P. Lamblin, D. Popovici, H. Larochelle, and Greedy layer-wise training of deep networks, in: Advances in neural information processing systems, pp. 153–160.

S. Hochreiter, J. Schmidhuber, Long short-term memory, Neural computation 9 (1997)1735–1780.

H. Anton, I. Bivens, S. Davis, Calculus, volume 2, Wiley Hoboken, 2002.H. Kutner, C. Nachtsheim, J. Neter, Applied linear regression models, McGrawHill/Irwin, 2004.

J. V. Basmajian, C. De Luca, Muscles alive, Muscles alive: their functions revealed byC. De Luca, Myoelectric manifestation of localized muscular fatigue in humans, in: IeeeTransactions on Biomedical Engineering, volume 30, IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC 445 HOES LANE, PISCATAWAY, NJ 08855 USA, pp.531–531.

Atzori, A. Gijsberts, C. Castellini, B. Caputo, A.-G. M. Hager, S. Elsig, G. Giatsidis, F. Bassetto, H. M¨uller, Electromyography data for non-invasive naturally-controlled Robotic hand prostheses, Scientific data 1 (2014) 140053.

M. Atzori, A. Gijsberts, I. Kuzborskij, S. Elsig, A.-G. M. Hager, O. Deriaz, C. Castellini,H. M¨uller, B. Caputo, Characterization of a benchmark database for myoelectric movement Classification, IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(2015) 73–83.

W. Geng, Y. Du, W. Jin, W. Wei, Y. Hu, J. Li, Gesture recognition by instantaneoussurface emg images, Scientific reports 6 (2016) 36571.

M. Wand, J. Schmidhuber, Deep neural network frontend for continuous emg-based speech recognition. In: INTERSPEECH, pp. 3032–3036.

M. Wand, M. Janke, T. Schultz, The emg-uka corpus for electromyographic speech processing, in: Fifteenth Annual Conference of the International Speech CommunicationAssociation, pp. 1593–1597.

M. Wand, T. Schultz, Pattern learning with deep neural networks in emg-based speech recognition, in: Engineering in Medicine and Biology Society (EMBC), 2014 36th AnnualInternational Conference of the IEEE, IEEE, pp. 4200–4203.

Y.-F. Chen, K. Atal, S. Xie, Q. Liu, A new multivariate empirical mode decomposition Method for improving the performance of ssvep-based brain computer interface, JournalOf Neural Engineering (2017).

T.-P. Jung, S. Makeig, M. Westerfield, J. Townsend, E. Courchesne, T. J. Sejnowski,Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects, Clinical Neurophysiology 111 (2000) 1745–1758.

J. R. Wolpaw, D. J. McFarland, G. W. Neat, C. A. Forneris, An eeg-based brain-computer Interface for cursor control, Electroencephalography and clinical neurophysiology 78 (1991)252–259.

S. C. Bunce, M. Izzetoglu, K. Izzetoglu, B. Onaral, K. Pourrezaei, Functional near-infrared Spectroscopy, IEEE engineering in medicine and biology magazine 25 (2006) 54–62.

U. R. Acharya, D. N. Ghista, Z. KuanYi, L. C. Min, E. Ng, S. V. Sree, O. Faust, L. Weidong, A. Alvin, Integrated index for cardiac arrythmias diagnosis using entropies as Features of heart rate variability signal, in: Biomedical Engineering (MECBME), 2011 1st Middle East Conference on, IEEE, pp. 371–374.

U. R. Acharya, O. Faust, N. A. Kadri, J. S. Suri, W. Yu, Automated identification of normal and diabetes heart rate signals using nonlinear measures, Computers in biology And medicine 43 (2013) 1523–1529.

O. Faust, Documenting and predicting topic changes in computers in biology and medicine:A bibliometric keyword analysis from 1990 to 2017, Informatics in Medicine Unlocked 11(2018) 15 – 27.

L. Fraiwan, K. Lweesy, Neonatal sleep state identification using deep learning autoencoders, in: Signal Processing & its Applications (CSPA), 2017 IEEE 13th International Colloquium on, IEEE, pp. 228–231.

M.-P. Hosseini, T. X. Tran, D. Pompili, K. Elisevich, H. Soltanian-Zadeh, Deep learningwith edge computing for localization of epileptogenicity using multimodal rs-fmri and eeg big data, in: Autonomic Computing (ICAC), 2017 IEEE International Conference on, IEEE, pp. 83–92.

R. T. Schirrmeister, L. D. J. Fiederer, J. T. Springenberg, M. Glasstetter, K. Eggensperger,M. Tangermann, F. Hutter, W. Burgard, T. Ball, Designing and understanding convolutional networks for decoding executed movements from eeg, in: The First BiannualNeuroadaptive Technology Conference, p. 143.

C. Spampinato, S. Palazzo, I. Kavasidis, D. Giordano, N. Souly, M. Shah, Deep learning human mind for automated visual classification, in: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4503–4511.

M. Winterhalder, T. Maiwald, H. Voss, R. Aschenbrenner-Scheibe, J. Timmer, A. SchulzeBonhage, The seizure prediction characteristic: a general framework to assess and compare seizure prediction methods, Epilepsy & Behavior 4 (2003) 318–325.

R. Aschenbrenner-Scheibe, T. Maiwald, M. Winterhalder, H. Voss, J. Timmer, and A. SchulzeBonhage, How well can epileptic seizures be predicted? An evaluation of a nonlinear Method, Brain 126 (2003) 2616–2626.

Maiwald, M. Winterhalder, R. Aschenbrenner-Scheibe, H. U. Voss, A. Schulze-Bonhage,J. Timmer, Comparison of three nonlinear seizure prediction methods by means of theseizure prediction characteristic, Physica D: nonlinear phenomena 194 (2004) 357–368.

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ARTICLE Published HISTORY

Submitted Date: 2023-05-23
Accepted Date: 2023-07-01
Published Date: 2023-07-11

How to Cite

Ahmad, A., Tariq , A. ., Hussain , H. K., & Yousaf Gill, A. . (2023). Revolutionizing Healthcare: How Deep Learning is poised to Change the Landscape of Medical Diagnosis and Treatment. Journal of Computer Networks, Architecture and High Performance Computing, 5(2), 458-471. https://doi.org/10.47709/cnahpc.v5i2.2350