## 9 Conclusions  
Reverse thermal simulations performed with the Genetic Algorithm-based optimization tool were used to estimate the personalized parameters of patients' breasts, such as thermal conductivity, density, specific heat, and blood perfusion coefficients in gland and fat layers, which included the breast's fat/gland contents along with 3D breast geometry and thermograms as inputs. The study was initially conducted with a homogeneous model using real patients' data to estimate initial guesses of tissue properties for further simulation with a multilayer model. Simulations were later performed using Comsol Multiphysics with MatLab Livelink and Solidworks Livelink in an integrated optimization loop. Firstly, based on temperature contours from the healthy breast, the personalized breast parameters were estimated and then used as initial guesses for further simulation with a multilayer model of the healthy and diseased breasts. Using those parameters, patient-specific/breast-specific parameters and contents of each layer were predicted by the reverse thermal simulation. Finally, based on the estimated specific parameters of each breast, further reverse thermal modeling simulation was performed using Genetic Algorithm and the temperature distribution on the skin as input in order to predict the depth and size of the tumor. The predicted parameters agreed well with doctor's diagnosis through palpation. A comparison of computed and IR thermographic temperature distributions shows general consistency as well as the very good agreement of predicted and measured temperatures at hotspots. Thus, it is concluded that the breast tumor diagnostic tool developed, which is based on patient-specific reverse thermal modeling, can accurately  
Figure 19: A 3D model with computed parameters (Patient No. 5). generate patient/breast-specific tissue properties and predict the size and location of the tumor in the breast. Future study will include the collection of a large volume of patients' data and the subsequent construction of thermal breast models for further validation and clinical trials. A Physics-Informed Neural Network (PINN) simulation tool will also be developed for comparison with the method developed for this study, and to allow for better prediction of the complex blood perfusion. More sophisticated breast models that contain different layers with an irregular distribution of vessels may be considered. Future study will also include the miniaturization of the equipment and the implementation of AI for BSE, as promoted by WHO. Our long term goal is to diagnose tumors with single-digit diameters and arbitrary depths during BSE. [PERSON] implemented the models in the software systems, performed the computations, and was involved in the writing of the paper; [PERSON]. ([PERSON]) was directly involved in the writing of the paper and managed the project acquisition; [PERSON] made intellectual contributions to the project and helped in the writing of the paper as the PI; [PERSON]. ([PERSON]) was mainly involved in the collection of patient data in the clinics and the training of doctors and nurses for the experiment; [PERSON] and [PERSON] made intellectual contributions to the project as collaborators and advisers and were directly involved in the writing of the paper. All authors have read and agreed to the published version of the manuscript. This research was funded by Ministry of Education and Science of the Republic of Kazakhstan, AP08857347 (\"Application of artificial intelligence to complement thermography for breast cancer prediction\"). The APC was funded by Ministry of Education and Science of the Republic of Kazakhstan. The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of Nazarbayev University the protocol code is 294/17062020 and approved on 20 July 2020. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Not applicable.