Evolution in Spinal Fracture Diagnosis and Brain Tumor Detection in the Last two Decades: A Timeline-Based Study
Abstract
It is well explained and proven the kind of impact that Artificial Intelligence (AI) has in the medical world. Especially in the field of diagnosis and prognosis AI has been found to be a very interesting tool. Diagnosis is the first and most important step as a part of the medical procedure. If the diagnosis is not accurate or not effective, the later stages of treatment are also impacted, and procedure becomes non-effective as an overall result. As far as the medical world is concerned, diagnosis majorly pertains to imaging modalities like Ultrasound, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET) and X-RAY. With the extensive research happening in the medical world today, and with the use of AI tools, a diagnosis can be made faster and with more accuracy. AI is widely used in signal processing, image processing, automating systems and processes. It is also used for analysing large sets of data and others alike. Hence, out of all other domains where AI is applied and used nowadays, medical diagnostics is one of the frontrunners
[1] A. S. Panayides, A. Amini, N. D. Filipovic, A. Sharma, S. A. Tsaftaris, A. Young, D. Foran, N. Do, S. Golemati, T. Kurc, K. Huang, K. S. Nikita, B. P. Veasey, M. Zervakis, J. H. Saltz, and C. S. Pattichis, “AI in Medical Imaging Informatics: Current Challenges and Future Directions”, IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 7, July 2020.
[2] D. Chen, Q. Fan, J. Liao, A. Aviles-Rivero, L. Yuan, N. Yu and G. Hua, “Controllable Image Processing via Adaptive Filter Bank Pyramid”, IEEE Transactions on Image Processing, vol. 29, pp. 8043-8054, July 2020.
[3] Y. Li, S. Wang, Y. Zhao, and Q. Ji, “Simultaneous Facial Feature Tracking and Facial Expression Recognition”, IEEE Transactions on Image Processing, vol. 22, no. 7, pp. 2559-2573, March 2013.
[4] C. Jing, T. Liang and Q. Lei, “Camera security network design and realization based on PCA facial recognition algorithms”, 2016 IEEE International Conference on Electronic Information and Communication Technology (ICEICT), pp. 12-15, August 2016.
[5] S. Shojaeilangari, W. Yau, K. Nandakumar, L. Jun and E. K. Teoh, “Robust Representation and Recognition of Facial Emotions Using Extreme Sparse Learning”, IEEE Transactions on Image Processing, vol. 24, no. 7, pp. 2140-2152, March 2015.
[6] R. Goebel, “Why Visualization is an AI-Complete Problem (and why that matters)”, 2016 20th International Conference Information Visualisation (IV), pp. 27-32, July 2016.
[7] Y. Chen, C. Schönlieb, P. Liò, T. Leiner, P. L. Dragotti, G. Wang, D. Rueckert, D. Firmin and G. Yang, “AI-Based Reconstruction for Fast MRI—A Systematic Review and Meta-Analysis”, Proceedings of the IEEE, vol. 110, no. 2, pp. 224-245, February 2022.
[8] G. Lammel, R. Dorsch, T. Giesselmann, J. Goldeck, J. Hahn, N. N. Hasan, J. Meier and K. Gandhi, “Smart System Architecture for Sensors with Integrated Signal Processing and AI”, 2021 Smart Systems Integration, pp. 1-4, July 2021.
[9] Y. Chen, C. Schönlieb, P. Liò, T. Leiner, P. L. Dragotti, G. Wang, D. Rueckert, D. Firmin and G. Yang, “AI-Based Reconstruction for Fast MRI—A Systematic Review and Meta-Analysis”, Proceedings of the IEEE, vol. 110, no. 2, pp. 224-245, February 2022.
[10] K. Tan, X. Xu and H. Bian, “The application of NDT algorithm in sonar image processing”, 2016 IEEE/OES China Ocean Acoustics (COA), pp. 1-4, January 2016.
[11] Y. Tai, Z. Sun, Z. Yao, “Content-Based Recommendation Using Machine Learning”, 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing, pp. 1-4, October 2021.
[12] R. U. Mustafa, D. Moura and C. E. Rothenberg, “Machine Learning Approach to Estimate Video QoE of Encrypted DASH Traffic in 5G Networks”, 2021 IEEE Statistical Signal Processing Workshop (SSP), pp. 586-589, July 2021.
[13] E. Vincent, “Is audio signal processing still useful in the era of machine learning?”, 2015 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), pp. 7-7, 2015.
[14] J. Liang and K. Wang, “Vibration Feature Extraction Using Audio Spectrum Analyzer Based Machine Learning”, 2017 International Conference on Information, Communication and Engineering (ICICE), pp. 381-384, 2017.
[15] K. S. Alqudaihi, N. Aslam, I. U. Khan, A. M. Almuhaideb, S. J. Alsunaidi, N. M. Abdel Rahman Ibrahim, F. A. Alhaidari, F. S. Shaikh, Y. M. Alsenbel, D. M. Alalharith, H. M. Alharthi, W. M. Alghamdi and M. S. Alshahrani, “Cough Sound Detection and Diagnosis Using Artificial Intelligence Techniques: Challenges and Opportunities”, IEEE Access, vol. 9, pp. 102327-102344, July 2021.
[16] F. Rong, “Audio Classification Method Based on Machine Learning”, 2016 International Conference on Intelligent Transportation, Big Data and Smart City (ICITBS), pp. 81-84, 2016.
[17] L. Brausch, S. Tretbar and H. Hewene, “Identification of advanced hepatic steatosis and fibrosis using ML algorithms on high-frequency ultrasound data in patients with non-alcoholic fatty liver disease”, 2021 IEEE UFFC Latin America Ultrasonic Symposium (LAUS), pp. 1-4, 2021.
[18] A. W. Salehi, P. Baglat, B. B. Sharma, G. Gupta and A. Upadhya, “A CNN Model: Earlier Diagnosis and Classification of Alzheimer Disease using MRI”, pp. 156-161, 2020.
[19] S. Nefoussi, A. Amamra and I. A. Amarouche, “A Comparative Study of Deep Learning Networks for COVID-19 Recognition in Chest X-ray Images”, 2020 2nd International Workshop on Human-Centric Smart Environments for Health and Well-being (IHSH), pp. 237-241, 2021.
[20] H. D. Rafik, “Classification and detection of covid-19 in human respiratory lungs using convolutional neural network architectures”, 2021 International Conference on Artificial Intelligence for Cyber Security Systems and Privacy (AI-CSP), pp. 1-10, 2021.
[21] S. Tummala and N. K. Focke, “Machine Learning Framework for Fully Automatic Quality Checking Of Rigid And Affine Registrations In Big Data Brain MRI”, 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 1734-1737, 2021.
[22] E. Alaoui, S. Tekouabou, S. Hartini, Z. Rustam, H. Silkan and S. Agoujil, “Improvement in automated diagnosis of soft tissues tumors using machine learning”, Big Data Mining and Analytics, vol. 4, no. 1, 2021.
[23] J. Hu, W. Whiteley, X. Zhang, C. Zhou and V. Panin, “Simultaneous Attenuation Correction, Scatter Correction, and Denoising in PET Imaging with Deep Learning”, 2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), pp. 1-3, 2020.
[24] B. Wang, G. Chen, L. Rong, A. Yu, T. Wen, Y. Zhang and B. Hu, “ECG diagnosis device based on machine learning”, 2021 IEEE International Conference on Emergency Science and Information Technology (ICESIT), pp. 383-386, 2021.
[25] S. Pirbhulal, N. Pombo, V. Felizardo, N. Garcia, A. H. Sodhro and S. C. Mukhopadhyay, “Towards Machine Learning Enabled Security Framework for IoT-based Healthcare”, 2019 13th International Conference on Sensing Technology (ICST), vol. 4, pp. 1-6, 2016.
[26] D. Panescu, “Medical device development”, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5591-5594, 2009.
[27] P. Nirmala, S. Ramesh, M. Tamilselvi, G. Ramkumar and G. Anitha, “An Artificial Intelligence enabled Smart Industrial Automation System based on Internet of Things Assistance”, 2022 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI), pp. 1-6, 2022.
[28] X. Xu, H. Dinkel, M. Wu and K. Yu, “Text-to-Audio Grounding: Building Correspondence Between Captions and Sound Events”, ICASSP 2021-2021 International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 606-610, 2021.
[29] Z. Li, S. Li and X. Luo, “Data-driven Industrial Robot Arm Calibration: A Machine Learning Perspective”, 2021 IEEE International Conference on Networking, Sensing and Control (ICNSC), vol. 1, pp. 1-6, 2021.
[30] F. Ahamed and F. Farid, “Applying Internet of Things and Machine-Learning for Personalized Healthcare: Issues and Challenges”, 2018 International Conference on Machine Learning and Data Engineering (ICMLDE), pp. 19-21, 2018.
[31] T. L. Pham, H. Nguyen, H. Nguyen, V. H. Nguyen, V. Bui and Y. M. Jang, “Object Detection Framework for High Mobility Vehicles Tracking in Night-Time”, 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), pp. 133-135, 2020.
[32] Y. Yang and L. Lin, “Automatic Pedestrians Segmentation Based on Machine Learning in Surveillance Video”, 2019 IEEE International Conference on Computational Electromagnetics (ICCEM), pp. 1-3, 2019.
[33] M. Igarashi, “Artificial Intelligence-assisted system development in gastrointestinal endoscopy and surgery”, 2020 Opto-Electronics and Communications Conference (OECC), pp. 1-3, 2020.
[34] S. Mansfield, E. Vin and K. Obraczka, “An IoT-Based System for Autonomous, Continuous, Real-Time Patient Monitoring and Its Application to Pressure Injury Management”, 2021 17th International Conference on Distributed Computing in Sensor Systems (DCOSS), pp. 66-68, 2021.
[35] E. T. Babar and M. U. Rahman, “A Smart, Low Cost, Wearable Technology for Remote Patient Monitoring”, IEEE Sensors Journal, vol. 21, no. 19, pp. 21947-21955, October 2021.
[36] P. Silapasuphakornwong and K. Uehira, “Smart Mirror for Elderly Emotion Monitoring”, 2021 IEEE 3rd Global Conference on Life Sciences and Technologies (LifeTech), pp. 356-359, 2021.
[37] M. Anastassova, “Is remote patient monitoring the future of healthcare?”, 2022 IEEE Zooming Innovation in Consumer Technologies Conference (ZINC), pp. 1-1, 2022.
[38] R. S. Nair, J. M. Lawrence, A. Vyakaranam, S. D. Svpk, S. Shameem, R. Sulo, A. A. Zainuddin, N. A. Noor and A. F. Mansor, “Recent Trends and Opportunities of Remote Multi- Parameter PMS using IoT”, 2021 3rd East Indonesia Conference on Computer and Information Technology (EIConCIT), pp. 325-329, 2021.
[39] R. V. Begum and K. Dharmarajan, “Cloud-Scope: A Modern Patient Monitoring and Analysis System”, 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), pp. 152-159, 2021.
[40] A. Horsch and T. Balbach, “Telemedical information systems”, IEEE Transactions on Information Technology in Biomedicine, vol. 3, no. 3, pp. 166-175, 1999.
[41] A. Musa, Y. Yusuf and M. Meckel, “A hospital resource and patient management system based on real-time data capture and intelligent decision making”, 2012 International Conference on Systems and Informatics (ICSAI2012), pp. 776-779, 2012.
[42] M. I. Fernández, P. Chanfreut, I. Jurado and J. M. Maestre, “A Data-Based Model Predictive Decision Support System for Inventory Management in Hospitals”, IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 6, pp. 2227-2236, 2021.
[43] M. Kalafat and A. Kakarountas, “A Smart Assistant Nightstand for Hospitals”, 2021 6th South-East European Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM), pp. 1-4, 2021.
[44] A. Pir, M. U. Akram and M. A. Khan, “Internet of things-based context awareness architectural framework for HMIS”, 2015 17th International Conference on E-health Networking, Application and Services (HealthCom), pp. 55-60, 2015.
[45] J. Macharia and C. Maroa, “Health management information systems (HMIS) implementation characteristics that influence the quality of healthcare in private hospitals in Kenya”, 2014 IST-Africa Conference Proceedings, pp. 1-12, 2014.
[46] M. Prasinos, I. Basdekis, M. Anisetti, G. Spanoudakis, D. Koutsouris and E. Damiani, “A Modelling Framework for Evidence-Based Public Health Policy Making”, IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 5, pp. 2388-2399, 2022.
[47] D. Ravì, C. Wong, F. Deligianni, M. Berthelot, J. Andreu-Perez, B. Lo and G. Yang, “Deep Learning for Health Informatics”, IEEE Journal of Biomedical and Health Informatics, vol. 21, no. 1, pp. 4-21, 2017.
[48] S. Harrer, “Artificial Intelligence for Clinical Trial Design”, 2020 IEEE Signal Processing in Medicine and Biology Symposium (SPBS), pp. 1-1, 2020.
[49] E. Hanada, K. Wada, K. Oda, K. Nishi and K. Kawazoe, “Practical use of Artificial Intelligence for Clinical Staff Other than Physicians”, 2018 IEEE 8th International Conference on Consumer Electronic- Berlin (ICCE- Berlin), pp. 1-4, 2018.
[50] E. Waltz, “AI takes its best shot: What AI can—and can't—do in the race for a coronavirus vaccine - [Vaccine]”, IEEE Spectrum, vol. 57, no. 10, pp. 24-67, 2020.
[51] P. Bhattacharya, D. Machi, J. Chen, S. Hoops, B. Lewis, H. Mortveit, S. Venkatramanan, M. L. Wilson, A. Marathe, P. Porebski, B. Klahn, J. Outten, A. Vullikanti, D. Xie, A. Adiga, S. Brown, C. Barrett and M. Marathe, “AI-Driven Agent-Based Models to Study the Role of Vaccine Acceptance in Controlling COVID-19 Spread in the US”, 2021 IEEE International Conference on Big Data (Big Data), pp. 1566-1574, 2021.
[52] C. Zhu and Y. Guan, “The Risks and Countermeasures of Accounting Artificial Intelligence”, 2022 3rd International Conference on Electronic Communication and Artificial Intelligence (IWECAI), pp. 358-361, 2022.
[53] S. K. Jagatheesaperumal, M. Rahouti, K. Ahmad, A. Al-Fuqaha and M. Guizani, “The Duo of Artificial Intelligence and Big Data for Industry 4.0: Applications, Techniques, Challenges, and Future Research Directions”, IEEE Internet of Things Journal, vol. 9, no. 15, pp. 12861-12885, 2022.
[54] M. ÖZARAR, A. Akansu and B. Hasbay, “Impact of Cyber Maturity Level on Health Sector”, 2021 International Conference on Information Security and Cryptology (ISCTURKEY), pp. 127-131, 2021.
[55] A. K. Rana, R. Krishna, S. Dhwan, S. Sharma and R. Gupta, “Review on Artificial Intelligence with Internet of Things - Problems, Challenges and Opportunities”, 2019 2nd International Conference on Power Energy, Environment and Intelligent Control (PEEIC), pp. 383-387, 2019.
[56] H. Sun, “Legal Examination and Regulation of Artificial Intelligence Algorithm”, 2021 International Conference on Computer Information Science and Artificial Intelligence (CISAI), pp. 588-591, 2021.
[57] Y. Zhao, C. Gong and Y. Wang, “Privacy Crisis in the Age of Artificial Intelligence and its Countermeasures”, 2021 7th Annual International Conference on Network and Information Systems for Computers (ICNISC), pp. 144-147, 2021.
[58] C. Gallese, C. Fuchs, S. G. Riva, E. Foglia, F. Schettini, L. Ferrario, E. Falletti and M. S. Nobile, “Predicting and Characterizing Legal Claims of Hospitals with Computational Intelligence: The Legal and Ethical Implications”, 2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), pp. 1-9, 2022
[2] D. Chen, Q. Fan, J. Liao, A. Aviles-Rivero, L. Yuan, N. Yu and G. Hua, “Controllable Image Processing via Adaptive Filter Bank Pyramid”, IEEE Transactions on Image Processing, vol. 29, pp. 8043-8054, July 2020.
[3] Y. Li, S. Wang, Y. Zhao, and Q. Ji, “Simultaneous Facial Feature Tracking and Facial Expression Recognition”, IEEE Transactions on Image Processing, vol. 22, no. 7, pp. 2559-2573, March 2013.
[4] C. Jing, T. Liang and Q. Lei, “Camera security network design and realization based on PCA facial recognition algorithms”, 2016 IEEE International Conference on Electronic Information and Communication Technology (ICEICT), pp. 12-15, August 2016.
[5] S. Shojaeilangari, W. Yau, K. Nandakumar, L. Jun and E. K. Teoh, “Robust Representation and Recognition of Facial Emotions Using Extreme Sparse Learning”, IEEE Transactions on Image Processing, vol. 24, no. 7, pp. 2140-2152, March 2015.
[6] R. Goebel, “Why Visualization is an AI-Complete Problem (and why that matters)”, 2016 20th International Conference Information Visualisation (IV), pp. 27-32, July 2016.
[7] Y. Chen, C. Schönlieb, P. Liò, T. Leiner, P. L. Dragotti, G. Wang, D. Rueckert, D. Firmin and G. Yang, “AI-Based Reconstruction for Fast MRI—A Systematic Review and Meta-Analysis”, Proceedings of the IEEE, vol. 110, no. 2, pp. 224-245, February 2022.
[8] G. Lammel, R. Dorsch, T. Giesselmann, J. Goldeck, J. Hahn, N. N. Hasan, J. Meier and K. Gandhi, “Smart System Architecture for Sensors with Integrated Signal Processing and AI”, 2021 Smart Systems Integration, pp. 1-4, July 2021.
[9] Y. Chen, C. Schönlieb, P. Liò, T. Leiner, P. L. Dragotti, G. Wang, D. Rueckert, D. Firmin and G. Yang, “AI-Based Reconstruction for Fast MRI—A Systematic Review and Meta-Analysis”, Proceedings of the IEEE, vol. 110, no. 2, pp. 224-245, February 2022.
[10] K. Tan, X. Xu and H. Bian, “The application of NDT algorithm in sonar image processing”, 2016 IEEE/OES China Ocean Acoustics (COA), pp. 1-4, January 2016.
[11] Y. Tai, Z. Sun, Z. Yao, “Content-Based Recommendation Using Machine Learning”, 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing, pp. 1-4, October 2021.
[12] R. U. Mustafa, D. Moura and C. E. Rothenberg, “Machine Learning Approach to Estimate Video QoE of Encrypted DASH Traffic in 5G Networks”, 2021 IEEE Statistical Signal Processing Workshop (SSP), pp. 586-589, July 2021.
[13] E. Vincent, “Is audio signal processing still useful in the era of machine learning?”, 2015 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), pp. 7-7, 2015.
[14] J. Liang and K. Wang, “Vibration Feature Extraction Using Audio Spectrum Analyzer Based Machine Learning”, 2017 International Conference on Information, Communication and Engineering (ICICE), pp. 381-384, 2017.
[15] K. S. Alqudaihi, N. Aslam, I. U. Khan, A. M. Almuhaideb, S. J. Alsunaidi, N. M. Abdel Rahman Ibrahim, F. A. Alhaidari, F. S. Shaikh, Y. M. Alsenbel, D. M. Alalharith, H. M. Alharthi, W. M. Alghamdi and M. S. Alshahrani, “Cough Sound Detection and Diagnosis Using Artificial Intelligence Techniques: Challenges and Opportunities”, IEEE Access, vol. 9, pp. 102327-102344, July 2021.
[16] F. Rong, “Audio Classification Method Based on Machine Learning”, 2016 International Conference on Intelligent Transportation, Big Data and Smart City (ICITBS), pp. 81-84, 2016.
[17] L. Brausch, S. Tretbar and H. Hewene, “Identification of advanced hepatic steatosis and fibrosis using ML algorithms on high-frequency ultrasound data in patients with non-alcoholic fatty liver disease”, 2021 IEEE UFFC Latin America Ultrasonic Symposium (LAUS), pp. 1-4, 2021.
[18] A. W. Salehi, P. Baglat, B. B. Sharma, G. Gupta and A. Upadhya, “A CNN Model: Earlier Diagnosis and Classification of Alzheimer Disease using MRI”, pp. 156-161, 2020.
[19] S. Nefoussi, A. Amamra and I. A. Amarouche, “A Comparative Study of Deep Learning Networks for COVID-19 Recognition in Chest X-ray Images”, 2020 2nd International Workshop on Human-Centric Smart Environments for Health and Well-being (IHSH), pp. 237-241, 2021.
[20] H. D. Rafik, “Classification and detection of covid-19 in human respiratory lungs using convolutional neural network architectures”, 2021 International Conference on Artificial Intelligence for Cyber Security Systems and Privacy (AI-CSP), pp. 1-10, 2021.
[21] S. Tummala and N. K. Focke, “Machine Learning Framework for Fully Automatic Quality Checking Of Rigid And Affine Registrations In Big Data Brain MRI”, 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 1734-1737, 2021.
[22] E. Alaoui, S. Tekouabou, S. Hartini, Z. Rustam, H. Silkan and S. Agoujil, “Improvement in automated diagnosis of soft tissues tumors using machine learning”, Big Data Mining and Analytics, vol. 4, no. 1, 2021.
[23] J. Hu, W. Whiteley, X. Zhang, C. Zhou and V. Panin, “Simultaneous Attenuation Correction, Scatter Correction, and Denoising in PET Imaging with Deep Learning”, 2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), pp. 1-3, 2020.
[24] B. Wang, G. Chen, L. Rong, A. Yu, T. Wen, Y. Zhang and B. Hu, “ECG diagnosis device based on machine learning”, 2021 IEEE International Conference on Emergency Science and Information Technology (ICESIT), pp. 383-386, 2021.
[25] S. Pirbhulal, N. Pombo, V. Felizardo, N. Garcia, A. H. Sodhro and S. C. Mukhopadhyay, “Towards Machine Learning Enabled Security Framework for IoT-based Healthcare”, 2019 13th International Conference on Sensing Technology (ICST), vol. 4, pp. 1-6, 2016.
[26] D. Panescu, “Medical device development”, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5591-5594, 2009.
[27] P. Nirmala, S. Ramesh, M. Tamilselvi, G. Ramkumar and G. Anitha, “An Artificial Intelligence enabled Smart Industrial Automation System based on Internet of Things Assistance”, 2022 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI), pp. 1-6, 2022.
[28] X. Xu, H. Dinkel, M. Wu and K. Yu, “Text-to-Audio Grounding: Building Correspondence Between Captions and Sound Events”, ICASSP 2021-2021 International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 606-610, 2021.
[29] Z. Li, S. Li and X. Luo, “Data-driven Industrial Robot Arm Calibration: A Machine Learning Perspective”, 2021 IEEE International Conference on Networking, Sensing and Control (ICNSC), vol. 1, pp. 1-6, 2021.
[30] F. Ahamed and F. Farid, “Applying Internet of Things and Machine-Learning for Personalized Healthcare: Issues and Challenges”, 2018 International Conference on Machine Learning and Data Engineering (ICMLDE), pp. 19-21, 2018.
[31] T. L. Pham, H. Nguyen, H. Nguyen, V. H. Nguyen, V. Bui and Y. M. Jang, “Object Detection Framework for High Mobility Vehicles Tracking in Night-Time”, 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), pp. 133-135, 2020.
[32] Y. Yang and L. Lin, “Automatic Pedestrians Segmentation Based on Machine Learning in Surveillance Video”, 2019 IEEE International Conference on Computational Electromagnetics (ICCEM), pp. 1-3, 2019.
[33] M. Igarashi, “Artificial Intelligence-assisted system development in gastrointestinal endoscopy and surgery”, 2020 Opto-Electronics and Communications Conference (OECC), pp. 1-3, 2020.
[34] S. Mansfield, E. Vin and K. Obraczka, “An IoT-Based System for Autonomous, Continuous, Real-Time Patient Monitoring and Its Application to Pressure Injury Management”, 2021 17th International Conference on Distributed Computing in Sensor Systems (DCOSS), pp. 66-68, 2021.
[35] E. T. Babar and M. U. Rahman, “A Smart, Low Cost, Wearable Technology for Remote Patient Monitoring”, IEEE Sensors Journal, vol. 21, no. 19, pp. 21947-21955, October 2021.
[36] P. Silapasuphakornwong and K. Uehira, “Smart Mirror for Elderly Emotion Monitoring”, 2021 IEEE 3rd Global Conference on Life Sciences and Technologies (LifeTech), pp. 356-359, 2021.
[37] M. Anastassova, “Is remote patient monitoring the future of healthcare?”, 2022 IEEE Zooming Innovation in Consumer Technologies Conference (ZINC), pp. 1-1, 2022.
[38] R. S. Nair, J. M. Lawrence, A. Vyakaranam, S. D. Svpk, S. Shameem, R. Sulo, A. A. Zainuddin, N. A. Noor and A. F. Mansor, “Recent Trends and Opportunities of Remote Multi- Parameter PMS using IoT”, 2021 3rd East Indonesia Conference on Computer and Information Technology (EIConCIT), pp. 325-329, 2021.
[39] R. V. Begum and K. Dharmarajan, “Cloud-Scope: A Modern Patient Monitoring and Analysis System”, 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), pp. 152-159, 2021.
[40] A. Horsch and T. Balbach, “Telemedical information systems”, IEEE Transactions on Information Technology in Biomedicine, vol. 3, no. 3, pp. 166-175, 1999.
[41] A. Musa, Y. Yusuf and M. Meckel, “A hospital resource and patient management system based on real-time data capture and intelligent decision making”, 2012 International Conference on Systems and Informatics (ICSAI2012), pp. 776-779, 2012.
[42] M. I. Fernández, P. Chanfreut, I. Jurado and J. M. Maestre, “A Data-Based Model Predictive Decision Support System for Inventory Management in Hospitals”, IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 6, pp. 2227-2236, 2021.
[43] M. Kalafat and A. Kakarountas, “A Smart Assistant Nightstand for Hospitals”, 2021 6th South-East European Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM), pp. 1-4, 2021.
[44] A. Pir, M. U. Akram and M. A. Khan, “Internet of things-based context awareness architectural framework for HMIS”, 2015 17th International Conference on E-health Networking, Application and Services (HealthCom), pp. 55-60, 2015.
[45] J. Macharia and C. Maroa, “Health management information systems (HMIS) implementation characteristics that influence the quality of healthcare in private hospitals in Kenya”, 2014 IST-Africa Conference Proceedings, pp. 1-12, 2014.
[46] M. Prasinos, I. Basdekis, M. Anisetti, G. Spanoudakis, D. Koutsouris and E. Damiani, “A Modelling Framework for Evidence-Based Public Health Policy Making”, IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 5, pp. 2388-2399, 2022.
[47] D. Ravì, C. Wong, F. Deligianni, M. Berthelot, J. Andreu-Perez, B. Lo and G. Yang, “Deep Learning for Health Informatics”, IEEE Journal of Biomedical and Health Informatics, vol. 21, no. 1, pp. 4-21, 2017.
[48] S. Harrer, “Artificial Intelligence for Clinical Trial Design”, 2020 IEEE Signal Processing in Medicine and Biology Symposium (SPBS), pp. 1-1, 2020.
[49] E. Hanada, K. Wada, K. Oda, K. Nishi and K. Kawazoe, “Practical use of Artificial Intelligence for Clinical Staff Other than Physicians”, 2018 IEEE 8th International Conference on Consumer Electronic- Berlin (ICCE- Berlin), pp. 1-4, 2018.
[50] E. Waltz, “AI takes its best shot: What AI can—and can't—do in the race for a coronavirus vaccine - [Vaccine]”, IEEE Spectrum, vol. 57, no. 10, pp. 24-67, 2020.
[51] P. Bhattacharya, D. Machi, J. Chen, S. Hoops, B. Lewis, H. Mortveit, S. Venkatramanan, M. L. Wilson, A. Marathe, P. Porebski, B. Klahn, J. Outten, A. Vullikanti, D. Xie, A. Adiga, S. Brown, C. Barrett and M. Marathe, “AI-Driven Agent-Based Models to Study the Role of Vaccine Acceptance in Controlling COVID-19 Spread in the US”, 2021 IEEE International Conference on Big Data (Big Data), pp. 1566-1574, 2021.
[52] C. Zhu and Y. Guan, “The Risks and Countermeasures of Accounting Artificial Intelligence”, 2022 3rd International Conference on Electronic Communication and Artificial Intelligence (IWECAI), pp. 358-361, 2022.
[53] S. K. Jagatheesaperumal, M. Rahouti, K. Ahmad, A. Al-Fuqaha and M. Guizani, “The Duo of Artificial Intelligence and Big Data for Industry 4.0: Applications, Techniques, Challenges, and Future Research Directions”, IEEE Internet of Things Journal, vol. 9, no. 15, pp. 12861-12885, 2022.
[54] M. ÖZARAR, A. Akansu and B. Hasbay, “Impact of Cyber Maturity Level on Health Sector”, 2021 International Conference on Information Security and Cryptology (ISCTURKEY), pp. 127-131, 2021.
[55] A. K. Rana, R. Krishna, S. Dhwan, S. Sharma and R. Gupta, “Review on Artificial Intelligence with Internet of Things - Problems, Challenges and Opportunities”, 2019 2nd International Conference on Power Energy, Environment and Intelligent Control (PEEIC), pp. 383-387, 2019.
[56] H. Sun, “Legal Examination and Regulation of Artificial Intelligence Algorithm”, 2021 International Conference on Computer Information Science and Artificial Intelligence (CISAI), pp. 588-591, 2021.
[57] Y. Zhao, C. Gong and Y. Wang, “Privacy Crisis in the Age of Artificial Intelligence and its Countermeasures”, 2021 7th Annual International Conference on Network and Information Systems for Computers (ICNISC), pp. 144-147, 2021.
[58] C. Gallese, C. Fuchs, S. G. Riva, E. Foglia, F. Schettini, L. Ferrario, E. Falletti and M. S. Nobile, “Predicting and Characterizing Legal Claims of Hospitals with Computational Intelligence: The Legal and Ethical Implications”, 2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), pp. 1-9, 2022
| Files | ||
| Issue | Vol 11 No 4 (2024) | |
| Section | Literature (Narrative) Review(s) | |
| DOI | https://doi.org/10.18502/fbt.v11i4.16516 | |
| Keywords | ||
| Artificial Intelligence Machine Learning Image Processing | ||
| Rights and permissions | |
|
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
How to Cite
1.
Gharote Y, Nagare G. Evolution in Spinal Fracture Diagnosis and Brain Tumor Detection in the Last two Decades: A Timeline-Based Study. Frontiers Biomed Technol. 2024;11(4):670-676.
