Thermal modelling and screening method for skin pathologies using active thermography

Breast Cancer Screening Using Inverse Modeling of Surface Temperatures and Steady-State Thermal Imaging

Cancer is characterized by increased metabolic activity and vascularity, leading to temperature changes in cancerous tissues compared to normal cells. This study focused on patients with abnormal mammogram findings or a clinical suspicion of breast cancer, exclusively those confirmed by biopsy. Utilizing an ultra-high sensitivity thermal camera and prone patient positioning, we measured surface temperatures integrated with an inverse modeling technique based on heat transfer principles to predict malignant breast lesions. Involving 25 breast tumors, our technique accurately predicted all tumors, with maximum errors below 5 mm in size and less than 1 cm in tumor location. Predictive efficacy was unaffected by tumor size, location, or breast density, with no aberrant predictions in the contralateral normal breast. Infrared temperature profiles and inverse modeling using both techniques successfully predicted breast cancer, highlighting its potential in breast cancer screening.

Thermal Time Constant CNN-Based Spectrometry for Biomedical Applications

This paper presents a novel method based on a convolutional neural network to recover thermal time constants from a temperature-time curve after thermal excitation. The thermal time constants are then used to detect the pathological states of the skin. The thermal system is modeled as a Foster Network consisting of R-C thermal elements. Each component is represented by a time constant and an amplitude that can be retrieved using the deep learning system. The presented method was verified on artificially generated training data and then tested on real, measured thermographic signals from a patient suffering from psoriasis. The results show proper estimation both in time constants and in temperature evaluation over time. The error of the recovered time constants is below 1% for noiseless input data, and it does not exceed 5% for noisy signals.

Learned Block Iterative Shrinkage Thresholding Algorithm for Photothermal Super Resolution Imaging

Block-sparse regularization is already well known in active thermal imaging and is used for multiple-measurement-based inverse problems. The main bottleneck of this method is the choice of regularization parameters which differs for each experiment. We show the benefits of using a learned block iterative shrinkage thresholding algorithm (LBISTA) that is able to learn the choice of regularization parameters, without the need to manually select them. In addition, LBISTA enables the determination of a suitable weight matrix to solve the underlying inverse problem. Therefore, in this paper we present LBISTA and compare it with state-of-the-art block iterative shrinkage thresholding using synthetically generated and experimental test data from active thermography for defect reconstruction. Our results show that the use of the learned block-sparse optimization approach provides smaller normalized mean square errors for a small fixed number of iterations. Thus, this allows us to improve the convergence speed and only needs a few iterations to generate accurate defect reconstruction in photothermal super-resolution imaging.

Towards an Accurate MRI Acute Ischemic Stroke Lesion Segmentation Based on Bioheat Equation and U-Net Model

Acute ischemic stroke represents a cerebrovascular disease, for which it is practical, albeit challenging to segment and differentiate infarct core from salvageable penumbra brain tissue. Ischemic stroke causes the variation of cerebral blood flow and heat generation due to metabolism. Therefore, the temperature is modified in the ischemic stroke region. In this paper, we incorporate acute ischemic stroke temperature profile to reinforce segmentation accuracy in MRI. Pennes bioheat equation was used to generate brain thermal images that may provide rich information regarding the temperature change in acute ischemic stroke lesions. The thermal images were generated by calculating the temperature of the brain with acute ischemic stroke. Then, U-Net was used in this paper for the segmentation of acute ischemic stroke. A dataset of 3192 images was created to train U-Net using k-fold crossvalidation. The training time was about 10 hours and 35 minutes in NVIDIA GPU. Next, the obtained trained model was compared with recent methods to analyze the effect of the ischemic stroke temperature profile in segmentation. The obtained results show that significant parts of acute ischemic stroke and background areas are segmented only in thermal images, which proves the importance of using thermal information to improve the segmentation outcomes in MRI diagnosis.

Automatic Classification of Foot Thermograms Using Machine Learning Techniques

Diabetic foot is one of the main complications observed in diabetic patients; it is associated with the development of foot ulcers and can lead to amputation. In order to diagnose these complications, specialists have to analyze several factors. To aid their decisions and help prevent mistakes, the resort to computer-assisted diagnostic systems using artificial intelligence techniques is gradually increasing. In this paper, two different models for the classification of thermograms of the feet of diabetic and healthy individuals are proposed and compared. In order to detect and classify abnormal changes in the plantar temperature, machine learning algorithms are used in both models. In the first model, the foot thermograms are classified into four classes: healthy and three categories for diabetics. The second model has two stages: in the first stage, the foot is classified as belonging to a diabetic or healthy individual, while, in the second stage, a classification refinement is conducted, classifying diabetic foot into three classes of progressive severity. The results show that both proposed models proved to be efficient, allowing us to classify a foot thermogram as belonging to a healthy or diabetic individual, with the diabetic ones divided into three classes; however, when compared, Model 2 outperforms Model 1 and allows for a better performance classification concerning the healthy category and the first class of diabetic individuals. These results demonstrate that the proposed methodology can be a tool to aid medical diagnosis.

Skin Cancer Detection Using Infrared Thermography: Measurement Setup, Procedure and Equipment

Infrared thermography technology has improved dramatically in recent years and is gaining renewed interest in the medical community for applications in skin tissue identification applications. However, there is still a need for an optimized measurement setup and protocol to obtain the most appropriate images for decision making and further processing. Nowadays, various cooling methods, measurement setups and cameras are used, but a general optimized cooling and measurement protocol has not been defined yet. In this literature review, an overview of different measurement setups, thermal excitation techniques and infrared camera equipment is given. It is possible to improve thermal images of skin lesions by choosing an appropriate cooling method, infrared camera and optimized measurement setup.

Low Cost Technologies that can be Integrated in the Medical Education in Emerging Areas

BACKGROUND

The situation created by global emergencies, such as the current global pandemic [1], has led in many parts of the world to the cessation of clinical practice in hospitals for medical and nursing students, although in medical education the contact with the patient and the practical practice of various medical techniques and procedures by contact is necessary. However, in situations of biological risk, it is natural to avoid any possibility of exposure of students.

MATERIAL AND METHODS

The objective is to analyze the possibility of creating low cost, modular telepresence robots, adapted for medical clinical practice and capable of allowing practicing students to interact remotely with patients, to be able to communicate with them, to be able to monitor them, to help them move, but also to be able to measure parameters such as temperature using thermal vision, pulse measurement or, the ability of the limbs to move. The robot consists of a mobile stretcher, with the possibility of moving both with wheels and with the help of legs, which allows the patient to move without human assistance in areas with obstacles. The method of achieving this goal refers to the realization of several modular applications and each to solve specific tasks, and finally to be interconnected in a telepresence robot that makes possible the remote interaction of practicing students with patients.

RESULTS

The research results emerge both from the prototypes made and from the simulations and the results of measuring certain vital parameters, sent by the telepresence robot via the internet to the medical team.

CONCLUSION

The idea of modular integration of several individual applications is feasible and offers the advantage of easily adapting to patients with various ailments and medical needs.

Processing of EMG Signals with High Impact of Power Line and Cardiac Interferences

This work deals with electromyography (EMG) signal processing for the diagnosis and therapy of different muscles. Because the correct muscle activity measurement of strongly noised EMG signals is the major hurdle in medical applications, a raw measured EMG signal should be cleaned of different factors like power network interference and ECG heartbeat. Unfortunately, there are no completed studies showing full multistage signal processing of EMG recordings. In this article, the authors propose an original algorithm to perform muscle activity measurements based on raw measurements. The effectiveness of the proposed algorithm for EMG signal measurement was validated by a portable EMG system developed as a part of the EU research project and EMG raw measurement sets. Examples of removing the parasitic interferences are presented for each stage of signal processing. Finally, it is shown that the proposed processing of EMG signals enables cleaning of the EMG signal with minimal loss of the diagnostic content.

NIR-II Fluorescence Imaging-Guided Photothermal Therapy with Amphiphilic Polypeptide Nanoparticles Encapsulating Organic NIR-II Dye

NIR-II fluorescence imaging-guided photothermal therapy is a potential tumor therapeutic that has exhibited accurate diagnosis and noninvasive therapy of tumors. Here, we developed an organic macromolecular nanoparticle (PFD) by encapsulating a fluorophore with an amphiphilic polypeptide. The PFD nanoparticle presented a uniform size of 70 nm with a slightly negative charge and exhibited superior photothermal conversion efficiency (40.69%), thermal imaging ability, and considerable photothermal stability. The PFD nanoparticle could accumulate at the tumor site by an enhanced penetration and retention effect and exhibited satisfactory fluorescence imaging and prominent photothermal inhibition effect. In vivo experiments demonstrated that PFD nanoparticles exhibited a prominent photothermal inhibition effect against the tumor. Meanwhile, the therapeutic procedure was monitored by both NIR-II fluorescence and infrared thermal imaging, which demonstrated that the PFD nanoparticles have a potential application in imaging-guided photothermal therapy of tumors.

Performance Assessment of Low-Cost Thermal Cameras for Medical Applications

* Correspondence: evilla@iac [...].