Temperature Distribution In Tissue Research Articles

Patient-specific temperature distribution prediction in laser interstitial thermal therapy: single-irradiation data-driven method

Laser interstitial thermal therapy (LITT) is popular for treating brain tumours and epilepsy. The strict control of tissue thermal damage extent is crucial for LITT. Temperature prediction is useful for predicting thermal damage extent. Accurately predicting in vivo brain tissue temperature is challenging due to the temperature dependence and the individual variations in tissue properties. Considering these factors is essential for improving the temperature prediction accuracy. Objective. To present a method for predicting patient-specific tissue temperature distribution within a target lesion area in the brain during LITT. Approach. A magnetic resonance temperature imaging (MRTI) data-driven estimation model was constructed and combined with a modified Pennes bioheat transfer equation (PBHE) to predict patient-specific temperature distribution. In the PBHE for temperature prediction, the individual specificity and temperature dependence of thermal tissue properties and blood perfusion, as well as the individual specificity of optical tissue properties were considered. Only MRTI data during one laser irradiation were required in the method. This enables the prediction of patient-specific temperature distribution and the resulting thermal damage region for subsequent ablations. Main results. Patient-specific temperature prediction was evaluated based on clinical data acquired during LITT in the brain, using intraoperative MRTI data as the reference standard. Our method significantly improved the prediction performance of temperature distribution and thermal damage region. The average root mean square error was decreased by 69.54%, the average intraclass correlation coefficient was increased by 37.5%, the average Dice similarity coefficient was increased by 43.14% for thermal damage region prediction. Significance. The proposed method can predict temperature distribution and thermal damage region at an individual patient level during LITT, providing a promising approach to assist in patient-specific treatment planning for LITT in the brain.

Review of the Capacity to Accurately Detect the Temperature of Human Skin Tissue Using the Microwave Radiation Method.

Microwave radiometry (MWR) is instrumental in detecting thermal variations in skin tissue before anatomical changes occur, proving particularly beneficial in the early diagnosis of cancer and inflammation. This study concisely traces the evolution of microwave radiometers within the medical sector. By analyzing a plethora of pertinent studies and contrasting their strengths, weaknesses, and performance metrics, this research identifies the primary factors limiting temperature measurement accuracy. The review establishes the critical technologies necessary to overcome these limitations, examines the current state and prospective advancements of each technology, and proposes comprehensive implementation strategies. The discussion elucidates that the precise measurement of human surface and subcutaneous tissue temperatures using an MWR system is a complex challenge, necessitating an integration of antenna directionality for temperature measurement, radiometer error correction, hardware configuration, and the calibration and precision of a multilayer tissue forward and inversion method. This study delves into the pivotal technologies for non-invasive human tissue temperature monitoring in the microwave frequency range, offering an effective approach for the precise assessment of human epidermal and subcutaneous temperatures, and develops a non-contact microwave protocol for gauging subcutaneous tissue temperature distribution. It is anticipated that mass-produced measurement systems will deliver substantial economic and societal benefits.

Enhancement of skin tumor laser hyperthermia with Ytterbium nanoparticles: numerical simulation

Laser hyperthermia therapy (HT) has emerged as a well-established method for treating cancer, yet it poses unique challenges in comprehending heat transfer dynamics within both healthy and cancerous tissues due to their intricate nature. This study investigates laser HT therapy as a promising avenue for addressing skin cancer. Employing two distinct near-infrared (NIR) laser beams at 980 nm, we analyze temperature variations within tumors, employing Pennes’ bioheat transfer equation as our fundamental investigative framework. Furthermore, our study delves into the influence of Ytterbium nanoparticles (YbNPs) on predicting temperature distributions in healthy and cancerous skin tissues. Our findings reveal that the application of YbNPs using a Gaussian beam shape results in a notable maximum temperature increase of 5 °C within the tumor compared to nanoparticle-free heating. Similarly, utilizing a flat top beam alongside YbNPs induces a temperature rise of 3 °C. While this research provides valuable insights into utilizing YbNPs with a Gaussian laser beam configuration for skin cancer treatment, a more thorough understanding could be attained through additional details on experimental parameters such as setup, exposure duration, and specific implications for skin cancer therapy.

Closed-loop high precision human tissue temperature measurements using a joint forward and inverse method

A closed-loop forward-inverse joint multi-layer temperature prediction method is introduced to overcome the limitations of non-contact microwave-based precise temperature measurements within human tissues. This approach merges an incoherent four-layer tissue forward model with a multi-frequency high-precision inversion algorithm. Random errors were incorporated into the forward model to construct a multi-layer human tissue dataset for performance validation and optimization of the inversion algorithm. The lack of precise temperature measurements in internal human tissues was addressed using an incoherent four-layer forward model that integrates the Pennes heat transfer equation with a fluid–solid coupling boundary modeling technique. Parameter differentiation analysis was conducted in the forward modeling step using incoherent electromagnetic transport equations. The proposed high-precision multi-frequency inversion process, added to the objective function, refines the XGBoost algorithm by assigning a penalty factor and adjusting for neighboring tissue temperature distributions. The Optuna framework was then utilized to optimize XGBoost hyper-parameter sets, resulting in the Opt-XGBoost inversion algorithm. This approach achieved a root mean square error of 0.033 °C and an average absolute error of 0.0256 °C in simulations involving forward modeling-generated data.

Non-Fourier Bioheat Transfer Analysis in Brain Tissue During Interstitial Laser Ablation: Analysis of Multiple Influential Factors

This work presents the dual-phase lag-based non-Fourier bioheat transfer model of brain tissue subjected to interstitial laser ablation. The finite element method has been utilized to predict the brain tissue's temperature distributions and ablation volumes. A sensitivity analysis has been conducted to quantify the effect of variations in the input laser power, treatment time, laser fiber diameter, laser wavelength, and non-Fourier phase lags. Notably, in this work, the temperature-dependent thermal properties of brain tissue have been considered. The developed model has been validated by comparing the temperature obtained from the numerical and ex vivo brain tissue during interstitial laser ablation. The ex vivo brain model has been further extended to in vivo settings by incorporating the blood perfusion effects. The results of the systematic analysis highlight the importance of considering temperature-dependent thermal properties of the brain tissue, non-Fourier behavior, and microvascular perfusion effects in the computational models for accurate predictions of the treatment outcomes during interstitial laser ablation, thereby minimizing the damage to surrounding healthy tissue. The developed model and parametric analysis reported in this study would assist in a more accurate and precise prediction of the temperature distribution, thus allowing to optimize the thermal dosage during laser therapy in the brain.

MODELING OF TEMPERATURE GRADIENTS DURING THE INTERACTION OF LASER RADIATION WITH BIOLOGICAL TISSUE

Modeling thermal gradients in biological tissues, when interacting with laser radiation, helps to understand how heat flows are distributed in the body and reflect heating zones. The main aspects of the simulation include the parameters of the laser source, such as the wavelength and power, the initial temperature conditions of the tissues, as well as the use of special numerical methods that help to optimally solve the heat conduction equations and visualize the obtained results. Temperature distribution in tissues plays an important role in determining the effectiveness and safety of laser treatment. This allows you to choose effective parameters of laser radiation and irradiation modes to achieve the desired result without negative consequences for the patient. However, it is important to consider that the actual behavior of tissues in response to laser radiation may be more complex due to the physiological characteristics of each person.Therefore, all models and calculations must take into account this feature and diversity and take into account safety regulations. Laser treatment should be carried out under the supervision of qualified specialists, in particular doctors who have relevant knowledge and experience in the field of medicine. This approach helps ensure the safety and effectiveness of laser procedures for patients. Modeling the distribution of tissue thermal gradients when interacting with laser radiation plays an important role in the development of modern medicine and scientific research and provides efficiency compared to the obtained experimental data. It helps to improve the understanding of thermal processes in biological tissues and to ensure the safety of patients receiving laser treatment. In this work, experiments were conducted on the effect of laser irradiation on biological tissue, as well as modeling of changes in temperature gradients during the effect of laser irradiation on biological tissue, using various models of heat and mass transfer, using the Matlab software environment. As a result of the modeling in this work, the patterns of changes in temperature gradients were obtained, which were compared with the experimentally obtained data, the errors of Pennes models and finite element methods, finite differences were analyzed, and conclusions were drawn.

Effect of geometric parameters of electrodes on skin heating for the design of non-ablative radiofrequency device.

Non-ablative radiofrequency (RF) has been widely used in clinical and at-home cosmetics devices. RF electrode geometry can influence the heat distribution in the tissue. This study analyzes the influence of geometric parameters of the electrode on the heat distribution in the layered tissue. The finite element simulation of the electrothermal coupling field was performed to obtain the three-dimensional (3D) temperature distribution of the four-layer tissue. The electrode geometric parameters including the inter-electrode spacing (5-12mm), width (1-3mm), length (3-10mm), shapes (bar, dot and circle), and the coupling gel's electrical conductivity (0.2-1.5 S/m) were simulated. The maximum temperature at 2mm depth (T-2mm ) and the temperature difference (Tdiff ) between the maximum skin surface temperature and T-2mm were obtained to evaluate the effectiveness and safety. The effect of geometric parameters on the effectiveness and safety was mixed. The maximum T-2mm occurred with the 5mm inter-electrode spacing, 3mm width, 10mm length, the circle-shaped electrode, and the 1.5 S/m coupling gel's electrical conductivity. The ratio of inter-electrode spacing to width at around four can achieve rapid temperature rise and skin surface temperature protection. The electrode shape influenced the area of temperature rise in the tissue's cross-section. The coupling gel's electrical conductivity should be close to that of the skin to avoid energy accumulation on the skin surface. The electrode's geometric parameters affect the effectiveness and safety of the RF product. This study has provided the simulation procedure for the electrode design.

Modified Pennes bioheat equation with heterogeneous blood perfusion: A newer perspective

The Pennes bioheat equation is a widely used mathematical model for predicting temperature distribution in biological tissues. However, it assumes homogeneous blood perfusion throughout the tissue, which may not accurately represent the complex perfusion patterns observed in tumors. In this study, we propose modification to traditional Pennes bioheat equation that incorporates the effects of perfusion heterogeneity by introducing a spatially varying perfusion term, represented by a perfusion coefficient, ωb(x,y,z) to account for local variations in blood flow at each spatial location within the biological tissue. Therefore, heterogeneous blood perfusion at each tumor tissue voxel location relax the uniform blood perfusion assumption. By incorporating these modifications, the model allows for a more realistic representation of the heat exchange between the biological tissue and blood flow, by predicting the non-uniform temperature distribution particularly in tumors with irregular blood vessel growth and organization, thereby facilitating treatment planning and optimization of thermal therapies, and leading to more effective therapeutic outcomes. The spatially varying perfusion coefficient can be obtained through advanced imaging techniques such as dynamic contrast-enhanced MRI or Doppler ultrasound enabling the assessment of perfusion heterogeneity in biological tissues. In conclusion, the modified Pennes bioheat equation offers a valuable tool for understanding the intricate relationship between with heterogeneous blood perfusion and temperature distribution in biological tissues, providing important insights to the thermal response of tumors and guiding the development of improved therapeutic strategies in optimizing cancer treatments.

Physics-informed neural network for fast prediction of temperature distributions in cancerous breasts as a potential efficient portable AI-based diagnostic tool

This work presents the development of a novel Physics-Informed Neural Network (PINN) method for fast forward simulation of heat transfer through cancerous breast models. The proposed PINN method combines deep learning and physical principles to predict the temperature distributions in breast tissues and identify potential abnormal regions indicating the presence of tumors. The PINN model is normally trained by physics in terms of the residuals of the heat transfer equation, as well as boundary conditions with and without datasets of surface thermal imaging data concerning cancerous breast tissues, which can be used for future inverse thermal modeling to calculate tumor sizes and locations. The model is validated by comparing its predictions with those obtained by traditional Finite Element Analysis (FEA) for various cases. The comparison validates the PINN model as an accurate and fast method for thermal modeling and breast cancer diagnostic tool as the PINN simulation is found to be around 12 times faster than its FEM counterpart. The utilization of deep learning and physical principles in a diagnostic tool provides a non-invasive and safer alternative for breast self-examination compared to traditional methods such as mammography. These findings hold promise for the ongoing development of a new portable Artificial Intelligence (AI) tool for the early detection of breast cancer in breast self-examination as promoted by WHO, which is crucial for reducing mortality rates of breast cancer in the world.

Temperature distribution in tumour tissue during targeted destruction by heat: A hyperbolic bioheat equation approach

Thermotherapy is a frequently used treatment to destroy malignant tumour. The procedure is carried-out using various techniques like radio frequency, laser, focused ultrasound, and microwaves to elevate the desired temperature at targeted tissues. However, it becomes indispensable to note that the impact of the high temperatures also expands to the peripherals of the targeted tissues and can harm the surrounding healthy tissue. This paper aims to find the temperature distribution in tumour tissue during the process and draw the corresponding graphical representation. A five-tine trocar has used to raise the temperature at the centre of the targeted tissue as much as to destroy the tumour tissue. A mathematical model based on the hyperbolic bioheat equation has been formulated and solved by variable separable method after converting the devised equation into dimensionless form. The heat waves make a significant contribution in the propagation of heat at high temperatures therefore, this paper can be viewed as an improvement of the models based on parabolic Penne’s bioheat equation. The results are helpful in estimating the temperature distribution on and around the targeted tissue and hence the therapist can be guided to prevent injuries during the treatment of therapeutic treatments.

Simulation study of mobile phone radiation thermal effect in human heart tissue

The public's concern over potential health risks related to the absorption of electromagnetic radiation (EMR) has been developing due to the prolonged use of cell phones close to the human body, particularly the human heart. To address these issues, this study aims to present a numerical simulation of EMR in the spectral range (900 MHz, 1800 MHz, and 2400 MHz) on human heart tissue using a Matlab program and the Finite-Difference Time-Domain (FDTD) method in One Dimension (1D). A mathematical analysis of electromagnetic radiation heating equations in a one-dimensional, one-layer model has also been discussed by numerically calculating the transient bioheat transfer equation and Maxwell's equations by using the FDTD to predict the effects of thermal physics properties on the transient temperature of human heart tissue. The results revealed that FDTD is an efficient method to evaluate the thermal effect of EMR due to its perfect boundary condition. It was found that the heart tissue reacts more at 900 MHz compared to 1800 MHz and 2400 MHz, and the tissue absorption is higher at the lower frequency. The effects of various parameters on the temperature increase in the human heart tissue were considered, such as the electric field, magnetic field, thicknesses, and thermal conductivity of the heart tissue. It was found that the frequency most affecting the tissue was 900 MHz. Our study found that the temperature distribution decreases with an increase in tissue thickness. We note that the results are similar for the different frequencies. It turns out that as the thermal conductivity of the heart tissue increases, the temperature distribution decreases slightly, while no change occurs here in the relationship between thermal conductivity and the temperature distribution of tissue with the change in the frequency of a wave.

Treatment Modelling of a 3D Tumour in Brain by Laser-Induced Interstitial Thermotherapy.

Introduction: There are some ways to examine heat transfer in tumor tissue, which is an important issue in bioengineering. One of these ways uses the bioheat equation, proposed by Pennes, in a continuous medium. Another one uses a porous medium to model heat transfer in living tissues. The objective of this paper was to study an approach to modelling the temperature distribution and tumour ablation in brain tissue and compare results to Pennes' approach. Methods: This approach presents and uses a porous medium as the tissue instead of a continuous medium. In addition, the two approaches (simulation in continuous and porous medium) are compared in terms of temperature simulation and amount of cell ablation. The density, heat conduction factor, and blood perfusion rate are considered functions of temperature. Results: In these approaches, after an 85-second treatment, the temperature increases to about 90°C. The temperature increase of the porous medium is relatively the same as that of the continuous medium and for this reason, the amount of cancerous cells that are ablated in a porous medium is approximately the same as that in a continuous medium. The volume of cell ablation is about 6500 mm3 for two ideas. In addition, the degree of damage, computed from the Arrhenius integral method, and the ablated volume of the tumour endorse equality at the end of treatment. According to the results, similar to the continuous approach, the porous approach predicts the temperature and amount of volume of damaged cells. Conclusion: Therefore, it is possible to use the porous approach instead of the Pennes approach for tumour treatment.

Thermal modulation of skin friction at the finger pad

Preliminary human studies show that reduced skin temperature minimises the risk of mechanically induced skin damage. However, the mechanisms by which cooling enhances skin tolerance to pressure and shear remain poorly understood. We hypothesized that skin cooling below thermo-neutral conditions will decrease kinetic friction at the skin-material interface. To test our hypothesis, we measured the friction coefficient of a thermally pre-conditioned index finger pad sliding at a normal load (5N) across a plate maintained at three different temperatures (38, 24, and 16 °C) in 8 healthy young adults (29±5y). To quantify the temperature distribution of the skin tissue, we used 3D surface scanning and Optical Coherence Tomography to develop an anatomically representative thermal model of the finger. Our group-level data indicated that the sliding finger with thermally affected tissues (up to 8 mm depth) experienced significantly lower frictional forces (p<0.01) at plate temperatures of 16 °C (i.e. 32% decrease) and 24 °C (i.e. 13% decrease) than at 38 °C, respectively. This phenomenon occurred consistently across participants (i.e. N = 6/8, 75%) and without large changes in skin hydration during sliding. Our complementary experimental and theoretical results provide new insights into thermal modulation of skin friction that can be employed for developing thermal technologies to maintain skin integrity under mechanical loading and shearing.

Laser Effects on Bioheat Transfer with Non-Newtonian Hybird Nanofluid Flow: Analytical Method with Finite Sine and Laplace Transforms

In this paper the effects of laser irradiation on MHD Non-Newtonian hybird nanofluid flow and bioheat transfer have been proposed. If the tissue is vertical and there is a sudden change in environmental temperature, free convection will flow and bioheat transfer must be solved in conjunction with hydrodynamics equations of nanofluid (blood) motion. The bioheat transfer within the tissue can be formulated in mathematical model as an initial and boundary value problem. The non-linear system of partial differential equations is solved analytically by applying Laplace transform with the help of finite Fourier sine transform. The energy equation assumes that the tissue temperature and blood phase are identical. The blood velocity profile is decreasing in parallel with the rise of fluid parameters. This implies that the medication conveyance therapy lessens the tumor volume and helps in annihilating malignancy cells by applying small parameters such as Casson parameter. The bioheat tissue temperature distribution increases as the both magnetite nanoparticles and multi-walled carbon nanotubes increase. Therefore, we enhance the physical properties of the blood by immersing the magnetite nanoparticles through it. The hybrid volume of nanoparticles will be more effective in enhancing blood velocity and tissue temperature by laser nanoparticle method.

Development of Novel Balloon-Integrated Optical Catheter for Endoscopic and Circumferential Laser Application.

The current study aims to demonstrate the feasibility of a novel balloon-integrated optical catheter (BIOC) to achieve endoscopic laser application for circumferential coagulation of a tubular tissue structure. Both optical and thermal numerical simulations were developed to predict the propagation of laser light and a spatio-temporal distribution of temperature in tissue. Ex vivo esophagus tissue was tested with 980nm laser light at 30 W for 90s for quantitative evaluations. In vivo porcine models were used to validate the performance of BIOC for circumferential and endoscopic laser coagulation of esophagus in terms of acute tissue responses post-irradiation. Optical simulations confirmed that a diffusing applicator was able to generate a circumferential light distribution in a tubular tissue structure. Both numerical and experimental results presented that the maximum temperature elevation occurred at 3-5mm (muscle layer) below the mucosa surface after 90 s irradiation. In vivo tests confirmed the circumferential delivery of laser light to a deep muscle layer as well as no evidence of thermal damage to the esophageal mucosa. The proposed BIOC can be a feasible optical device to provide circumferential laser irradiation as well as endoscopic coagulation of tubular esophagus tissue for clinical applications.

Thermal responses of human tissues during nano-assisted hyperthermia for subcutaneous tumors

Non-invasive thermal therapy for subcutaneous tumors with the assistance of gold nanoparticles and surface cooling measures was proposed in the present study. To achieve real-time monitoring of 3-D temperature distribution and accurate measurement of thermal response, we developed a theoretical approach in which the dual-phase-lag (DPL) biological heat transfer model and Henriques' burn estimation model were employed. A closed-form solution for temperature distribution in the skin tissue can be obtained from this new model by employing the Green's function method. In addition, the influences of the embedment of gold nanoparticles, surface cooling, and non-Fourier effects on the temperature, thermal damage, and size of the burnt region were discussed in detail. The present model performs well in addressing the difficult issue of temperature and burn prediction inside human tissue. It is found that the combination of gold nanoparticles and surface cooling can concentrate thermal empyrosis in the subcutaneous tumor to enhance tumor elimination and protect both skin and deeper healthy tissue. This study provides theoretical support for the improvement and development of laser thermotherapy for subcutaneous tumors.

Identification of tissue and therapy parameters from surface temperature.

The temperature distribution of human body tissues, when electromagnetic waves are applied, is studied with the help of the bioheat equation, which is characterised by several thermal parameters, such as thermal conductivity, perfusion frequency or metabolic heat. There are also electromagnetic parameters, such as electrical conductivity and dielectric constant. Besides, therapy parameters, such as the applied power should be considered. If the values of all these parameters are known, the time evolution of the temperature can be determined. Some of these values can be found in specialised databases, and others are unknown but approximate values can be obtained by reasonable estimations. The outcome of simulations depends heavily on the parameter values. Surface temperature helps in providing better estimates of the parameter values. Once these parameters are identified, medical analyses can be performed to assess the dosimetry of the radiation so that it does not damage tissues, for example. The surface temperature is obtained from a sequence of thermographic images. Based on these experimental data, an algorithm is applied to find the values of the needed parameters. The model is simulated iteratively, adjusting the parameters at each step, reducing the approximation error between the simulation and the data. This is an optimization problem that belongs to the realm of inverse problems. It can be solved using techniques based on the gradient concept, however, this problem can be ill-conditioned, so probabilistic or evolutionary algorithms are also used. In this paper, the simulation is made using a method based on Legendre wavelets. It is proposed that the subsequent optimization is made using an evolutionary algorithm, that has shown great robustness in the problems where it has been applied. As far as known, it has never been applied to the bioheat equation. This is the aim of this work.

Numerical study of high-intensity focused ultrasound (HIFU) in fat reduction.

This study aimed to investigate the effect of fat-layer thickness and focal depth on the pressure and temperature distribution of tissue. Computer simulations were performed for the skin-fat layer models during high-intensity focused ultrasound (HIFU) treatment. The acoustic pressure field was calculated using the nonlinear Westervelt equation and coupled with the Pennes bioheat transfer equation to obtain the temperature distribution. To investigate the effect of the thickness of the fat layer on pressure and thermal distributions, the thickness of the fat layer behind the focal point (z=13.5mm) changed from 8 to 24mm by 2mm step. The pressure and temperature distribution spectra were extracted. The simulated results were validated using the experimental results with a 98% correlation coefficient (p<0.05). There was a significant difference between the pressure amplitude and temperature distribution for the 8-14mm thickness of the fat layer (p<0.05). By changing the focal point from 11.5 to 13.5mm, the maximum acoustic pressure at the focal point increased 66%, and the maximum temperature was 56%, respectively. Considering the specific treatment plan for each patient, according to the skin and fat layer thicknesses, can help prevent side effects and optimize the treatment process of HIFU.

A novel electrode to achieve balance between anastomotic strength and tissue thermal damage for radiofrequency-induced intestinal anastomosis

Purpose This paper aimed to develop a novel electrode to achieve balance between anastomotic strength and tissue thermal damage for radiofrequency-induced intestinal anastomosis. Methods The mechanical properties of the novel electrode were analyzed by finite element method, and then the temperature and thermal damage distribution of intestinal tissue during welding process were analyzed by electric-thermal-mechanical multi-field coupled finite element analysis. In ex-vivo experiments, the biomechanical strength of anastomotic area was assessed by indexes of tensile force and burst pressure. A thermocouple probe and an infrared thermal imager were used to monitor the temperature and thermal damage of the intestinal tissue in experiments. Furthermore, histopathological examination and transmission electron microscopy observation were used to observe the morphology and microstructural of anastomotic area. Results A slightly higher mean biomechanical strength is acquired with the tensile force and burst pressure results increasing from 9.7±1.47 N, 84.0±5.99 mmHg to 11.8±2.01 N, 89.6±6.79 mmHg respectively as well as the percentage of necrotic tissue caused by thermal damage decreasing from 89% to 33% for the novel electrode group, with compression force of 20 N, radiofrequency (RF) energy of 120 W and welding duration of 8 s applied to the target regions to achieve anastomosis. Moreover, tightly connected intestinal tissue with collagenic crosslink in the fusion area could be observed by histopathological examination and transmission electron microscopy. Conclusion Our study shows that the proposed novel electrode could achieve balance between anastomotic strength and tissue thermal damage for radiofrequency-induced intestinal anastomosis.

Breast Cancer Diagnosis Using Artificial Intelligence Approaches: A Systematic Literature Review

One of the most prevalent cancers in women is breast cancer. Early and accurate detection can decrease the mortality rate associated with breast cancer. Governments and health organizations emphasize the significance of early breast cancer screening since it is associated to a greater variety of available treatments and a higher chance of survival. Patients have the best chance of obtaining effective treatment when they are diagnosed early. The detection and diagnosis of breast cancer have involved using various image types and imaging modalities. Breast “infrared thermal” imaging is one of the imaging modalities., a screening instrument used to measure the temperature distribution of breast tissue, and even though it has not been used as extensively as mammography it demonstrated encouraging outcomes when utilized for early detection. It also has several advantages, as it is safe, painless, non-invasive, and inexpensive. The literature showed that the use of thermal images with deep neural networks improved the accuracy of early diagnosis of breast malformations. Therefore, in this paper, we aim to provide a systematic review of research efforts and state-of-the-art studies in the domain of breast cancer detection using AI techniques. The review highlighted different issues, such as using different imaging modalities and deep attention mechanisms with deep learning (DL), which proved to enhance detection accuracy.