<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Meraghni, Safa</style></author><author><style face="normal" font="default" size="100%">Benaggoune, Khaled</style></author><author><style face="normal" font="default" size="100%">Al-Masry, Zeina</style></author><author><style face="normal" font="default" size="100%">Terrissa, Labib</style></author><author><style face="normal" font="default" size="100%">Devalland, Christine</style></author><author><style face="normal" font="default" size="100%">Zerhouni, Noureddine</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards Digital Twins Driven Breast Cancer Detection</style></title><secondary-title><style face="normal" font="default" size="100%"> Lecture Notes in Networks and Systems </style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://link.springer.com/chapter/10.1007/978-3-030-80129-8_7</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">285</style></volume><pages><style face="normal" font="default" size="100%">87–99</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	Digital twins have transformed the industrial world by changing the development phase of a product or the use of equipment. With the digital twin, the object’s evolution data allows us to anticipate and optimize its performance. Healthcare is in the midst of a digital transition towards personalized, predictive, preventive, and participatory medicine. The digital twin is one of the key tools of this change. In this work, DT is proposed for the diagnosis of breast cancer based on breast skin temperature. Research has focused on thermography as a non-invasive scanning solution for breast cancer diagnosis. However, body temperature is influenced by many factors, such as breast anatomy, physiological functions, blood pressure, etc. The proposed DT updates the bio-heat model’s temperature using the data collected by temperature sensors and complementary data from smart devices. Consequently, the proposed DT is personalized using the collected data to reflect the person’s behavior with whom it is connected.
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