HE-05 A machine learning powered wearable imaging sensor for cancer detection
Creators: Zhiyi Sun
Publisher: name: IEEE
Year: 2025

Considerable progress has been achieved in the development of functional flexible materials, forming the foundation of flexible and wearable electronics. [1-3] These materials have undergone substantial advancements over the past decade. Based on this, we have designed a versatile multifunctional wearable imaging system with machine-learning algorithm for cancer detection as shown in Fig 1. The data services to enable sensor imaging and optimize pre- and post-operative care decisions for breast cancer will be provided in the future. Here, we present findings concerning the design and efficacy of a wearable, wireless patch designed for real-time imaging, employing flexible magnetoelectric nanoparticle (MENP) technology. [4,5] It integrates advanced magnetoelectric sensors for real-time imaging, capable of capturing highly detailed images that can be used for early detection of breast cancer. The device marks a substantial breakthrough in wearable sensing technology, offering direct practical applications in medical device market. It aims to minimize discomfort, increase screening frequency options, and improve early diagnosis in the future. As shown in Fig 2, sensor signals are acquired via ADC, processed, and converted into DAC output amplitudes to achieve real-time updates of DAC outputs. With further optimization, the system can integrate machine learning algorithms and this wearable devices to deliver appropriate and actionable data in 3D format to the patients and physicians.References: [1] Li, W., Liu, H., Wu, F. 2023 8th International Conference on Information Systems Engineering (ICISE), Dalian, China, 2023, pp. 167-170, doi: 10.1109/ICISE60366.2023.00041. [2] He, T., C. Lee, C. IEEE Open Journal of Circuits and Systems, vol. 2, pp. 702-720, 2021, doi: 10.1109/OJCAS.2021.3123272. [3] Meng, H., Zhong, W., Ma, K. Engineered Regeneration, vol. 5, issue 2, pp. 186-198, 2024, ISSN 2666-1381, doi: 10.1016/j.engreg.2024.03.003. [4] Song, X. ACS Nano 18(26): 16648-16657, 2024. [5] Tu, J., Min, J., Song, Y. Nat. Biomed. Eng., vol. 7, pp. 1293-1306, 2023. [6] Park, H. IEEE Transactions on Biomedical Circuits and Systems, vol. 17, no. 5, pp. 889-899, Oct. 2023, doi: 10.1109/TBCAS.2023.3288126.
Considerable progress has been achieved in the development of functional flexible materials, forming the foundation of flexible and wearable electronics. [1-3] These materials have undergone substantial advancements over the past decade. Based on this, we have designed a versatile multifunctional wearable imaging system with machine-learning algorithm for cancer detection as shown in Fig 1. The data services to enable sensor imaging and optimize pre- and post-operative care decisions for breast cancer will be provided in the future. Here, we present findings concerning the design and efficacy of a wearable, wireless patch designed for real-time imaging, employing flexible magnetoelectric nanoparticle (MENP) technology. [4,5] It integrates advanced magnetoelectric sensors for real-time imaging, capable of capturing highly detailed images that can be used for early detection of breast cancer. The device marks a substantial breakthrough in wearable sensing technology, offering direct practical applications in medical device market. It aims to minimize discomfort, increase screening frequency options, and improve early diagnosis in the future. As shown in Fig 2, sensor signals are acquired via ADC, processed, and converted into DAC output amplitudes to achieve real-time updates of DAC outputs. With further optimization, the system can integrate machine learning algorithms and this wearable devices to deliver appropriate and actionable data in 3D format to the patients and physicians.References: [1] Li, W., Liu, H., Wu, F. 2023 8th International Conference on Information Systems Engineering (ICISE), Dalian, China, 2023, pp. 167-170, doi: 10.1109/ICISE60366.2023.00041. [2] He, T., C. Lee, C. IEEE Open Journal of Circuits and Systems, vol. 2, pp. 702-720, 2021, doi: 10.1109/OJCAS.2021.3123272. [3] Meng, H., Zhong, W., Ma, K. Engineered Regeneration, vol. 5, issue 2, pp. 186-198, 2024, ISSN 2666-1381, doi: 10.1016/j.engreg.2024.03.003. [4] Song, X. ACS Nano 18(26): 16648-16657, 2024. [5] Tu, J., Min, J., Song, Y. Nat. Biomed. Eng., vol. 7, pp. 1293-1306, 2023. [6] Park, H. IEEE Transactions on Biomedical Circuits and Systems, vol. 17, no. 5, pp. 889-899, Oct. 2023, doi: 10.1109/TBCAS.2023.3288126.