Файловый архив студентов. Файловый архив студентов.
1329 вузов, 5502 предметов.
/ Регистрация
FAQ Обратная связь Вопросы и предложения
Добавил:
kiopkiopkiop18@yandex.ru t.me/Prokururor I Вовсе не секретарь, но почту проверяю Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Ростовский Государственный Медицинский Университет
Предмет:
Медицина общая
Файл:

Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5234_Библиотеки_им_академика_М_И_Перельмана

.pdf
Скачиваний:
0
Добавлен:
15.09.2026
Размер:
16 Мб
Скачать
☆
304 Computational Intelligence Algorithms
FIGURE 19.9 Model communication cost comparison.
communication. However, DenseNet requires higher data transfer of 1.1 GB per round, and ResNet communicates at 1.2 GB. Among the four models, Inception v3 has the highest communication cost of 1.3 GB per round, as it has a complex archi­tecture. The plot in Figure 19.9 shows that EfcientNetB2 is more advantageous to consider for training models for deep FL for detecting AD classication.
Figure 19.10 illustrates the efciency of different optimization algorithms applied
in FL with the combination of EfcientNetB2. GWO attained good federated
FIGURE 19.10 Comparison of Federated Learning accuracies of EfcientNetB2 with dif­ferent evolutionary algorithms.
305 Optimizing Digital Healthcare for Alzheimer’s Disease
FIGURE 19.11 Comparison of federated learning accuracies of ResNet with different evo-
lutionary algorithms.
accuracy at 84.5% among other algorithms with client results. These observations show that GWO is the most efcient optimization algorithm.
The above Figure 19.11 shows the results for optimization algorithms for ResNet in FL, where GWO has achieved the highest federated accuracy of 85.5%. Figure 19.12 shows DenseNet with various optimization algorithms and their federated accuracies.
FIGURE 19.12 Comparison of federated learning accuracies of dense net with different evolutionary algorithms.
306 Computational Intelligence Algorithms
FIGURE 19.13 Comparison of federated learning accuracies of inception V3 with different
evolutionary algorithms.
Figure 19.13 shows the federated accuracy results for optimization algorithms for
Inception v3 in FL with various evolutionary optimization algorithms.
The bar plot depicted in Fig ure 19.14 displays the accuracy comparison of dif­ferent federated DL models from various studies from previous reseach, includ­ing a proposed model. The data comprise four entries: Li et al. (2022) with CNN,
FIGURE 19.14 Comparison of existing methodologies with proposed DFLCNN.
307 Optimizing Digital Healthcare for Alzheimer’s Disease
Zhang et al. (2023) with MobileNet, Wang et al. (2024) with VGG, and the proposed DFLCNN model. It shows that DFLCNN achieves the highest accuracy of 92.2%, signicantly outperforming the other models. MobileNet follows with an accuracy of 89.0%, VGG at 88.5%, and CNN at 85.0%.
19.8 CONCLUSION
In this chapter, we have explored the applications of federated DL models for classi­cation of AD, specically applying EfcientNetB2 and optimizing the model with the GWO algorithm. The obtained results demonstrated that this scheme yields bet­ter performance metrics, where accuracy is 92.2%, a loss is 0.29, and achieved an F1 score with value of 0.89. In addition, the cost of model communication is effectively managed at 0.9 GB, which highlights its efciency in a deep FL environment. These ndings specify that by optimizing advanced neural networks with dense layers, we can achieve high accuracy and lower model communication cost, which is an encour­aging solution for real-world applications in distributed environments, like cloud and fog environments that receive data from various digital healthcare systems. Despite these results, DFLCNNS is limited to a specic dataset. Future research can be focused on applying evolutionary optimization algorithms to different datasets with different parameter settings. Additionally, the research can be elaborated by validat­ing model performance on biomarkers datasets.
REFERENCES
1. Alzheimer’s Association. (2023). 2023 Alzheimer’s Disease Facts and Figures. Alzheimer’s & Dementia. Retrieved from Alzheimer’s Association. https://www.
alz.org/getmedia/76e51bb6-c003-4d84-8019-e0779d8c4e8d/alzheimers-facts-and­gures.pdf
2. Nicolini, C., & Fahnestock, M. (2024). Pathophysiology of Alzheimer’s disease: From amyloid plaques to tau tangles. Current Alzheimer Research, 21(2), 198–212.
3. Liu, Y., Wang, J., & Liu, J. (2024). The role of APOE ε4 in Alzheimer’s disease: Current understanding and future perspectives. Journal of Alzheimer’s Disease, 90(1), 123–135.
4. Miller, R. C., & Iadecola, C. (2023). Inammation and oxidative stress in Alzheimer’s disease: Insights from recent studies. Neurobiology of Aging, 116, 234–249.
5. Angelopoulou, E., & Papageorgiou, S. G. (2025). Telemedicine in Alzheimer’s disease and other dementias: Where we are? Journal of Alzheimer’s Disease, 103(1), 3–18.
https://doi.org/10.1177/13872877241298295
6. López, L., Green, A. R., Tan-McGrory, A., King, R. S., & Betancourt, J. R. (2011). Bridging the digital divide in health care: The role of health information technology in addressing racial and ethnic disparities. The Joint Commission Journal on Quality and Patient Safety, 37(10), 437–445. https://doi.org/10.1016/S1553-7250(11)37055-9.
7. Bhargavi, M. S., & Prabhakar, B., “Deep learning approaches for early detection of Alzheimer’s disease using MRI neuroimaging,” 2022 International Conference on Connected Systems & Intelligence (CSI), Trivandrum, India, 2022, pp. 1–6. https://doi.
org/10.1109/CSI54720.2022.9924058.
8. Pallawi, S., & Singh, D. K., “Detection of Alzheimer’s disease stages using pre-trained deep learning approaches,” 2023 IEEE 5th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), Hamburg, Germany, 2023, pp. 252–256. htt ps://doi.org/10.1109/ ICCCMLA58983.2023.10346730.
308 Computational Intelligence Algorithms
9. Singh Chhabra, G., Guru, A., Rajput, B. J., Dewangan, L., & Swarnkar, S. K., “Multimodal neuroimaging for early Alzheimer’s detection: A deep learning approach,” 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), Delhi, India, 2023, pp. 1–5. https://doi.org/10.1109/
ICCCNT56998.2023.10307780.
10. Elgendy, O., & Nassif, A. B., “Alzheimer detection using different deep learning methods with MRI images,” 2023 Advances in Science and Engineering Technology International Conferences (ASET), Dubai, United Arab Emirates, 2023, pp. 1–6.
https://doi.org/10.1109/ASET56582.2023.10180640.
11. Trivedi, N. K., Jain, S., & Agarwal, S., “Identifying and categorizing Alzheimer’s dis­ease with lightweight federated learning using identically distributed images,” 2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, India, 2024, pp. 1–5. https://doi.
org/10.1109/ICRITO61523.2024.10522428.
12. Sampath, R., & Baskar, M., “An optimized deep learning approach to identify the Alzheimer’s stages identication based on biomarkers extraction,” 2023 Intelligent Computing and Control for Engineering and Business Systems (ICCEBS), Chennai, India, 2023, pp. 1–6. https://doi.org/10.1109/ ICCEBS58601.2023.10449183.
13. S, S., G, M., & Sherly, E., “Alzheimer’s disease classication from cross-sectional brain MRI using deep learning,” 2022 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES), Thiruvananthapuram, India, 2022, pp. 401–405. https://doi.org/10.1109/SPICES52834.2022.9774135.
14. Arya, A. D., Singh Verma, S., Chakarabarti, P., & Bishnoi, R., “Prediction of Alzheimer’s disease - A machine lear ning perspective with ensemble lear ning,” 2023 6th Internationa l Conference on Contemporary Computing and Informatics (IC3I), Gautam Buddha Nagar, India, 2023, pp. 2308–2313. https://doi.org/10.1109/ IC3I59117.2023.10397683.
15. Prabhakar, B., & Bhargavi, M. S., “A machine learning model to identify Best blood plasma proteins for early detection of Alzheimer’s disease,” 2022 IEEE 4th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), Goa, India, 2022, pp. 190–195. https://doi.org/10.1109/ICCCMLA56841.2022.9989117.
16. Moorthy, D. K., P, N., & Subhashini, S. J., “A review on Alzheimer’s disease detec­tion using machine learning,” 2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS), Trichy, India, 2023, pp. 573–581.
https://doi.org/10.1109/ ICAISS58487.2023.10250457.
17. Luz, S., Haider, F., & De Sousa, P., “Machine learning models for detection and assess­ment of progression in Alzheimer’s disease based on blood and cerebrospinal uid biomarkers,” 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Sydney, Australia, 2023, pp. 1–4. https://doi.
org/10.1109/EMBC40787.2023.10341203.
18. Khadatka r, D. R., & Patra, J. P. (2023). “Comparative analysis of different machi ne learni ng algorithms for detection of Alzheimer disease from medical images,” 2023 International Conference on Articial Intelligence for Innovations in Healthcare Industries (ICAIIHI), Raipur, India, pp. 1–5. https://doi.org/10.1109/ICAIIHI57871.2023.10489217
19. Irfan, M., Shahrestani, S., & Elkhodr, M., “Early detection of Alzheimer’s disease using cognitive features: A voting-based ensemble machine learning approach,” in IEEE Engineering Management Review, 51(1), pp. 16–25, 1 Firstquarter, March 2023.
https://doi.org/10.1109/ EMR.2022.3230820.
20. Saxena, A., & Kaur, H., “Alzheimer’s Disease (AD) Detection Using Various Machine Learning Techniques: A Systematic Review,” 2023 6th International Conference on Contemporary Computing and Informatics (IC3I), Gautam Buddha Nagar, India, 2023, pp. 77–81. https://doi.org/10.1109/ IC3I59117.2023.10397889.
309 Optimizing Digital Healthcare for Alzheimer’s Disease
21. U, B., & S, C., “Computer aided Alzheimer’s disease diagnosis from brain imaging dataset - A review,” 2023 Intelligent Computing and Control for Engineering and Business Systems (ICCEBS), Chennai, India, 2023, pp. 1–6. https://doi.org/10.1109/
ICCEBS58601.2023.10449067.
22. Mandawkar, U., & Diwan, T. (2024). Hybrid cuttle sh-grey wolf optimiza­tion tuned weighted ensemble classier for Alzheimer’s disease classication. Biomedical Signal Processing and Control, 92, 106101. https://doi.org/10.1016/j.
bspc.2024.106101.
23. Hcini et al. (2024). Investigating deep learning for early detection and decision­making in Alzheimer’s disease: A comprehensive review. Neural Process Letters 56, 153. https://doi.org/10.1007/s11063-024-11600-5.
24. Uddin et al. (2023). A novel approach utilizing machine learning for the early diagno­sis of Alzheimer’s disease. Biomedical Materials & Devices, 1, 882–898. https://doi.
org/10.1007/s44174-023-00078-9.
25. Pyrrho, M., Cambraia, L., & de Vasconcelos, V.F. (2022). Privacy and health practices in the digital age. The American Journal of Bioethics, 22(7), 50–59. https://doi.org/10.1
080/15265161.2022.2040648.
26. Posircaru DR, Serbanati LD. Integrating legacy medical applications in a standardized electronic health record platform. In2015 E-Health and Bioengineering Conference (EHB) 2015 Nov 19 (pp. 1–4). IEEE.
27. Doll, J., Malloy, J., & Bland, J. (2021, July/August ). The promise of interoperability. The American Journal of Occupational Therapy, 75(4), 7504090010. https://doi.
org/10.5014/ajot.2021.049002
28. Dabbs, A. D. V., Myers, B. A., McCurry, K. R., Dunbar-Jacob, J., Hawkins, R. P., Begey, A., & Dew, M. A. (2009). User-centered design and interactive health technolo­gies for patients. CIN: Computers, Informatics, Nursing, 27(3), 175–183. https://doi.
org/10.1097/ NCN.0b013e31819f7c7c
29. Grande, D., Luna Marti, X., Feuerstein-Simon, R., Merchant, R.M., Asch, D.A., Lewson, A., & Cannuscio, C.C. (2020, July) Health policy and privacy challenges associated with digital technology. JAMA Netw Open, 3(7), e208285. https://doi.
org/10.1001/jamanetworkopen.2020.8285.
30. Liu, Y., et al. (2022). Computational challenges in deep learning for Alzheimer’s dis­ease: A comprehensive review. Neurocomputing, 488, 413–424.
31. Zhang, H., et al. (2023). Data fusion techniques for integrating heterogeneous data in Alzheimer’s disease research. Journal of Biomedical Informatics, 134, 104193.
32. Li, X., et al. (2022). Telemedicine in Alzheimer's disease and other dementias: An updated overview. Dementia & Neuropsychologia, 16(2), 150–165.
33. Shen, W., et al. (2022). Telehealth infrastructure, Accountable Care Organization, and Medicare payment reduction. Telemedicine and e-Health, 28(9), 1234–1240.
34. Kim, J., et al. (2022). Emerging roles of telemedicine in dementia treatment and care. Dementia & Neuropsychologia, 16(3), 200–210.
35. Kumar, R., et al. (2021). Telemedicine and dementia care: A systematic review of bar­riers and facilitators. Journal of the American Medical Directors Association, 22(6), 1234–1240.
36. Guo, J., et al. (2023). Dynamic resource management in cloud and fog computing for healthcare applications. Future Generation Computer Systems, 135, 379–391.
37. Chen, L., et al. (2022). Scalable cloud and fog computing architectures for large-scale Alzheimer’s disease data analysis. Journal of Parallel and Distributed Computing, 160, 49–60.
38. Li, T., et al. (2023). Federated learning: Privacy and security perspectives. IEEE Transactions on Information Forensics and Security, 18, 485–498.
310 Computational Intelligence Algorithms
39. Wen, J., et al. (2022). Convolutional neural networks for detecting Alzheimer’s disease: A comprehensive review. Neurocomputing, 447, 108–120.
40. Liu, Y., et al. (2023). Recurrent neural networks for modeling cognitive decline in Alzheimer’s disease. Journal of Alzheimer’s Disease, 85(1), 123–134.
41. Vaswani, A., et al. (2021). Applications of transformer models in Alzheimer’s dis­ease research. IEEE Transactions on Neural Networks and Learning Systems, 32(9), 4283–4295.
42. Schraudolph, N. N., et al. (2023). Efcient quasi-Newton methods for training deep neural networks. Journal of Machine Learning Research, 24, 1–25.
43. Snoek, J., Larochelle, H., & Adams, R. P. (2012). Practical Bayesian optimization of machine learning algorithms. Advances in Neural Information Processing Systems, 25, 2951–2959.
44. Zhang, H., et al. (2021). Telehealth delivery of evidence-based intervention within older adult populations: A scoping review. Journal of Applied Gerontology, 40(12), 1234–1245.
Articial Intelligence
20
A Game-Changer in Parkinson’s Disease Neurorehabilitation
Nabeela Rehman, Arshya Anwar, and Sahar Zaidi
20.1 INTRODUCTION
Parkinson’s disease (PD) is a neurodegenerative, extrapyramidal, progressive, chronic disorder of movement characterized by a reduction of dopamine-producing neurons [1, 2]. Recent epidemiological research has provided new insight into the rising incidence and pattern of this debilitating condition around the world. According to the Global Burden of Disease (GBD) report, the number of people with PD increased by 118% to 6.2 million between 1990 and 2015. It is expected that this concerning pattern will persist, with over 12 million people worldwide estimated to have PD by 2040 [3, 4]. Notably, the disease is associated with motor symptoms like rigidity, tremors, bradykinesia, postural instability, muscle stiffness, and coordination issues, all of which can limit a patient’s movement and indepen­dence [5].
Articial intelligence (AI) has shown signicant potential in the diagnosis and treatment of disease. With high accuracy rates ranging from 93.88% to 96.27%, many AI models, including a support vector machine, random forest, and decision tree, are used to assess PD. Early and precise prediction is essential, and these mod­els help with that [6, 7].
Furthermore, AI methods assist in evaluating disease severity and stage, produc­ing reliable evaluations necessary for PD patients’ appropriate treatment and moni­toring [8]. Automated assessment of motor and gait impairments is one such use; this is an important rst step toward early identication of the disorder [9]. Researchers can now examine and compare walking patterns between PD patients and healthy individuals owing to the development of autoregressive algorithms that can extract information from gait signals [10].
By accurately classifying patients based on their speech and language patterns, AI − especially deep learning − also helps develop speech biomarkers for the assess­ment of disorders and provides efcacy for speech rehabilitation [11–13]. Several studies have emphasized the practical importance of AI methods, like computer vision and machine learning algorithms, in precisely detecting PD based on small variations in motor control in handwriting patterns [14]. These AI-driven methods extract information from handwriting photos, such as spectral properties, pressure
DO I: 10.1201/ 97810 03520 34 4 -23
311
312 Computational Intelligence Algorithms
FIGURE 20.1 Projected global burden of Parkinson disease, 1990–2040.
data, and kinematic parameters to precisely differentiate PD patients from healthy individuals [15]. Current machine learning techniques have demonstrated encourag­ing outcomes in accurately diagnosing PD, offering a useful adjunct to traditional clinical evaluation approaches. AI allows for proactive intervention in the manage­ment of the disease and greatly improves diagnostic precision by integrating clinical data with machine learning algorithms [6]. Additionally, AI-powered rehabilitation programmers can be very helpful in assisting people with PD to regain and maintain their independence and mobility. The development of specialized rehabilitation pro­grams that are tailored to the particular needs of every individual can be provided.
This chapter will talk about AI-based evaluation and treatment strategies along with robotic rehabilitation for gait management, machine learning models for speech rehabilitation, and the role of virtual reality in maintaining a healthy diet and sleep and performing an adequate amount of physical activity to improve the quality of life of people with the disease. Figure 20.1 shows projections for the growth in the use of these strategies.
20.2 INTRODUCTION OF INTELLIGENCE IN PARKINSON’S DISEASE
20.2.1 AI-BASED GAIT EVALUATION AND REHABILITATION
A modern instance of technological innovation is the virtualization of rehabilitation. In the evaluation and management of PD, AI-based technologies have become increasingly important in recent years [16, 17 ]. Furthermore, PD biomarkers for analysis of posture during the gait cycle can be used by various devices with machine learning and deep learning features to carry out automated detection [18]. Numerous AI models, including
313 Articial Intelligence
machine learning techniques, smartphone applications, sensory-based technology, and data on nocturnal breathing, are utilized to study and identify PD [19]. Gait analysis and assistive technology have been combined, allowing sensor-equipped devices to rec­ognize and predict risks associated with falling (freezing of gait) to prevent falls or reduce their impact [18, 20]. Excellent outcomes have been observed when using iner­tial measurement units (IMUs) to observe the advancement of PD and identify specic gait anomalies in individuals (see Figure 20.2). Research has shown that IMU-based models of gait assessment are capable of quantifying a wide variety of gait factors, such as spatiotemporal parameters, joint kinematics, variability, asymmetry, and stabil­ity, and can accurately distinguish between healthy controls and early-stage PD [21]. Wearable IMUs have also been used to track motor characteristics of PD, identify uc­tuations in motor performance, dyskinesia, and freezing of gait, and evaluate treatment responsiveness in outdoor environments, demonstrating its potential as a tool for ongo­ing monitoring and disease rehabilitation [22]. One well-known disorder that frequently harms a person’s quality of life is gait festination. The pooling techniques have been heavily utilized by convolutional neural networks (CNN) in particular for their deep learning strategies. In fog prediction, recurrent neural networks (RNNs), CNNs, and other distinct neural network subtypes have also been extensively used [23].
AI models like the hybrid ConvNet-Transformer architecture can accurately diag­nose PD severity stages from gait data, leveraging the strengths of CNNs and trans­formers to capture both local features and long-term spatiotemporal dependencies in the data [24]. Levodopa administration combined with any type of implantable pulse generator (IPG) stimulation results in a signicant improvement on posture and also average step height and step length on each side [25].
Also, in terms of PD gait evaluation and rehabilitation, random forest outper­formed naïve Bayes, which had an accuracy of 84.6% in diagnosing the disease, while random forest performed exceptionally well in identifying its stages [26].
FIGURE 20.2 Signicance of ML algorithms for PD diagnosis and stage identication by analysing gait parameters.
Соседние файлы в папке Библиотека им академика М.И. Перельмана
Помощь Обратная связь Вопросы и предложения Пользовательское соглашение Политика конфиденциальности