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Monopolar electrocoagulation is a well-established surgical technique to achieve hemostasis by selectively destroying biological tissue through the application of high-frequency alternating current. However, this technique is associated with unwanted tissue damage. In this context, computational simulation is a valuable tool that can improve our understanding of such complex processes and highlight important application parameters in the direction of an improved control function to achieve safer and more reliable results. Despite its critical role in surgical applications, the influence of the electrode-tissue contact area has received little to no attention in previous simulation studies. To address this gap, the present study investigates the sensitivity of temperature distribution and necrotic volume formation to variations in electrode-tissue contact area. For this purpose, a multiphysics finite element model was developed to simulate HF current induced soft coagulation using a ball electrode under varying contact areas. Our findings demonstrate that variations in the contact area significantly impact temperature development and, consequently, necrosis formation. These results highlight the crucial role of the contact area in the electrocoagulation process and its associated necrosis formation. Furthermore, it was observed that when the boiling point of water is reached inside the tissue, complete necrosis has not yet formed at the contact site, which could lead to further undesired effects. Consequently, it is essential to consider the contact area in computational simulations and the development of novel control features for safer and more reliable electrocoagulation.

Artificial intelligence (AI) has shown its potential to advance applications in various medical fields. One such area involves developing integrated AI-based systems to assist in laparoscopic surgery. Surgical tool detection and phase recognition are key components to develop such systems, and therefore, they have been extensively studied in recent years. Despite significant advancements in this field, previous image-based methods still face many challenges that limit their performance due to complex surgical scenes and limited annotated data. This study proposes a novel deep learning approach for classifying and localizing surgical tools in laparoscopic surgeries. The proposed approach uses a self-supervised learning algorithm for surgical tool classification followed by a weakly supervised algorithm for surgical tool localization, eliminating the need for explicit localization annotation. In particular, we leverage the Bidirectional Encoder Representation from Image Transformers (BEiT) model for tool classification and then utilize the heat maps generated from the multi-headed attention layers in the BEiT model for the localizing of these tools. Furthermore, the model incorporates class weights to address the class imbalance issue resulting from different usage frequencies of surgical tools in surgeries. Evaluated on the Cholec80 benchmark dataset, the proposed approach demonstrated high performance in surgical tool classification, surpassing previous works that utilize both spatial and temporal information. Additionally, the proposed weakly supervised learning approach achieved state-of-the-art results for the localization task.

The measurement of tidal volumes via respiratory-induced surface movements of the upper body has been an objective in medical diagnostics for decades, but a real breakthrough has not yet been achieved. The improvement of measurement technology through new, improved sensor systems and the use of artificial intelligence have given this field of research a new dynamic in recent years and opened up new possibilities. Based on the measurement from a motion capture system, the respiration-induced surface motions of 16 test subjects were examined, and specific motion parameters were calculated. Subsequently, linear regression and a tailored convolutional neural network (CNN) were used to determine tidal volumes from an optimal set of motion parameters. The results showed that the linear regression approach, after individual calibration, could be used in clinical applications for 13/16 subjects (mean absolute error < 150 mL), while the CNN approach achieved this accuracy in 5/16 subjects. Here, the individual subject-specific calibration provides significant advantages for the linear regression approach compared to the CNN, which does not require calibration. A larger dataset may allow for greater confidence in the outcomes of the CNN approach. A CNN model trained on a larger dataset would improve performance and may enable clinical use. However, the database of 16 subjects only allows for low-risk use in home care or sports. The CNN approach can currently be used to monitor respiration in home care or competitive sports, while it has the potential to be used in clinical applications if based on a larger dataset that could be gradually built up. Thus, a CNN could provide tidal volumes, the missing parameter in vital signs monitoring, without calibration.

Emotional intelligence strives to bridge the gap between human and machine interactions. The application of such systems varies and is becoming more prominent as healthcare services seek to provide more efficient care by utilizing smart digital health apps. One application in digital health is the incorporation of emotion recognition systems as a tool for therapeutic interventions. To this end, a system is designed to collect and analyze physiological signal data, such as electrodermal activity (EDA) and electrocardiogram (ECG), from smart wearable devices. The data are collected from different subjects of varying ages taking part in a study on emotion induction methods. The obtained signals are processed to identify stimulus trigger instances and classify the different reaction stages, as well as arousal strength, using signal processing and machine learning techniques. The reaction stages are identified using a support vector machine algorithm, while the arousal strength is classified using the ResNet50 network architecture. The findings indicate that the EDA signal effectively identifies the emotional trigger, registering a root mean squared error (RMSE) of 0.9871. The features collected from the ECG signal show efficient emotion detection with 94.19% accuracy. However, arousal strength classification is only able to reach 60.37% accuracy on the given dataset. The proposed system effectively detects emotional reactions and can categorize their arousal strength in response to specific stimuli. Such a system could be integrated into therapeutic settings to monitor patients’ emotional responses during therapy sessions. This real-time feedback can guide therapists in adjusting their strategies or interventions.

Critical care, like many healthcare areas, is under a dual assault from significantly increasing demographic and economic pressures. Intensive care unit (ICU) patients are highly variable in response to treatment, and increasingly aging populations mean ICUs are under increasing demand and their cohorts are increasingly ill. Equally, patient expectations are growing, while the economic ability to deliver care to all is declining. Better, more productive care is thus the big challenge. One means to that end is personalised care designed to manage the significant inter- and intra-patient variability that makes the ICU patient difficult. Thus, moving from current “ one size fits all ” protocolised care to adaptive, model-based “ one method fits all ” personalised care could deliver the required step change in the quality, and simultaneously the productivity and cost, of care. Computer models of human physiology are a unique tool to personalise care, as they can couple clinical data with mathematical methods to create subject-specific models and virtual patients to design new, personalised and more optimal protocols, as well as to guide care in real-time. They rely on identifying time varying patient-specific parameters in the model that capture inter- and intra-patient variability, the difference between patients and the evolution of patient condition. Properly validated, virtual patients represent the real patients, and can be used in silico to test different protocols or interventions, or in real-time to guide care. Hence, the underlying models and methods create the foundation for next generation care, as well as a tool for safely and rapidly developing personalised treatment protocols over large virtual cohorts using virtual trials. This review examines the models and methods used to create virtual patients. Specifically, it presents the models types and structures used and the data required. It then covers how to validate the resulting virtual patients and trials, and how these virtual trials can help design and optimise clinical trial. Links between these models and higher order, more complex physiome models are also discussed. In each section, it explores the progress reported up to date, especially on core ICU therapies in glycemic, circulatory and mechanical ventilation management, where high cost and frequency of occurrence provide a significant opportunity for model-based methods to have measurable clinical and economic impact. The outcomes are readily generalised to other areas of medical care.

Electrical Impedance Tomography (EIT) is a low-cost imaging method which reconstructs two-dimensional cross-sectional images, visualising the impedance change within the thorax. However, the reconstruction of an EIT image is an ill-posed inverse problem. In addition, blurring, anatomical alignment, and reconstruction artefacts can hinder the interpretation of EIT images. In this contribution, we introduce a patient-specific structural prior mask into the EIT reconstruction process, with the aim of improving image interpretability. Such a prior mask ensures that only conductivity changes within the lung regions are reconstructed. To evaluate the influence of the introduced structural prior mask, we conducted numerical simulations with two scopes in terms of their different ventilation statuses and varying atelectasis scales. Quantitative analysis, including the reconstruction error and figures of merit, was applied in the evaluation procedure. The results show that the morphological structures of the lungs introduced by the mask are preserved in the EIT reconstructions and the reconstruction artefacts are decreased, reducing the reconstruction error by 25.9% and 17.7%, respectively, in the two EIT algorithms included in this contribution. The use of the structural prior mask conclusively improves the interpretability of the EIT images, which could facilitate better diagnosis and decision-making in clinical settings.

Structural prior information can improve electrical impedance tomography (EIT) reconstruction. In this contribution, we introduce a discrete cosine transformation-based (DCT-based) EIT reconstruction algorithm to demonstrate a way to incorporate the structural prior with the EIT reconstruction process. Structural prior information is obtained from other available imaging methods, e.g., thorax-CT. The DCT-based approach creates a functional EIT image of regional lung ventilation while preserving the introduced structural information. This leads to an easier interpretation in clinical settings while maintaining the advantages of EIT in terms of bedside monitoring during mechanical ventilation. Structural priors introduced in the DCT-based approach are of two categories in terms of different levels of information included: a contour prior only differentiates lung and non-lung region, while a detail prior includes information, such as atelectasis, within the lung area. To demonstrate the increased interpretability of the EIT image through structural prior in the DCT-based approach, the DCT-based reconstructions were compared with reconstructions from a widely applied one-step Gauss-Newton solver with background prior and from the advanced GREIT algorithm. The comparisons were conducted both on simulation data and retrospective patient data. In the simulation, we used two sets of forward models to simulate different lung conditions. A contour prior and a detail prior were derived from simulation ground truth. With these two structural priors, the reconstructions from the DCT-based approach were compared with the reconstructions from both the one-step Gauss-Newton solver and the GREIT. The difference between the reconstructions and the simulation ground truth is calculated by the ℓ 2 -norm image difference. In retrospective patient data analysis, datasets from six lung disease patients were included. For each patient, a detail prior was derived from the patient’s CT, respectively. The detail prior was used for the reconstructions using the DCT-based approach, which was compared with the reconstructions from the GREIT. The reconstructions from the DCT-based approach are more comprehensive and interpretable in terms of preserving the structure specified by the priors, both in simulation and retrospective patient data analysis. In simulation analysis, the ℓ 2 -norm image difference of the DCT-based approach with a contour prior decreased on average by 34% from GREIT and 49% from the Gauss-Newton solver with background prior; for reconstructions of the DCT-based approach with detail prior, on average the ℓ 2 -norm image difference is 53% less than GREIT and 63% less than the reconstruction with background prior. In retrospective patient data analysis, the reconstructions from both the DCT-based approach and GREIT can indicate the current patient status, but the DCT-based approach yields more interpretable results. However, it is worth noting that the preserved structure in the DCT-based approach is derived from another imaging method, not from the EIT measurement. If the structural prior is outdated or wrong, the result might be misleadingly interpreted, which induces false clinical conclusions. Further research in terms of evaluating the validity of the structural prior and detecting the outdated prior is necessary.

Adapting intelligent context-aware systems (CAS) to future operating rooms (OR) aims to improve situational awareness and provide surgical decision support systems to medical teams. CAS analyzes data streams from available devices during surgery and communicates real-time knowledge to clinicians. Indeed, recent advances in computer vision and machine learning, particularly deep learning, paved the way for extensive research to develop CAS. In this work, a deep learning approach for analyzing laparoscopic videos for surgical phase recognition, tool classification, and weakly-supervised tool localization in laparoscopic videos was proposed. The ResNet-50 convolutional neural network (CNN) architecture was adapted by adding attention modules and fusing features from multiple stages to generate better-focused, generalized, and well-representative features. Then, a multi-map convolutional layer followed by tool-wise and spatial pooling operations was utilized to perform tool localization and generate tool presence confidences. Finally, the long short-term memory (LSTM) network was employed to model temporal information and perform tool classification and phase recognition. The proposed approach was evaluated on the Cholec80 dataset. The experimental results (i.e., 88.5% and 89.0% mean precision and recall for phase recognition, respectively, 95.6% mean average precision for tool presence detection, and a 70.1% F1-score for tool localization) demonstrated the ability of the model to learn discriminative features for all tasks. The performances revealed the importance of integrating attention modules and multi-stage feature fusion for more robust and precise detection of surgical phases and tools.

Lung EIT is a functional imaging method that utilizes electrical currents to reconstruct images of conductivity changes inside the thorax. This technique is radiation free and applicable at the bedside, but lacks of spatial resolution compared to morphological imaging methods such as X-ray computed tomography (CT). In this article we describe an approach for EIT image reconstruction using morphologic information obtained from other structural imaging modalities. This leads to recon- structed images of lung ventilation that can easily be superimposed with structural CT or MRI images, which facilitates image interpretation. The approach is based on a Discrete Cosine Transformation (DCT) of an image of the considered transversal thorax slice. The use of DCT enables reduction of the dimensionality of the reconstruction and ensures that only conductivity changes of the lungs are reconstructed and displayed. The DCT based approach is well suited to fuse morphological image information with functional lung imaging at low computational costs. Results on simulated data indicate that this approach preserves the morphological structures of the lungs and avoids blurring of the solution. Images from patient measurements reveal the capabilities of the method and demonstrate benefits in possible applications.

Health apps have increased in popularity as people increasingly follow the advice these apps provide to enhance physical and mental well-being. One key aspect of improving neurosensory health is identifying and expressing emotions. Emotional intelligence is crucial for maintaining and enhancing social interactions. In this context, a preliminary closed-loop feedback system has been developed to help people project specific emotions by altering their facial expressions. This system is part of a research intervention aimed at therapeutic applications for individuals with autism spectrum disorder. The proposed system functions as a digital mirror, initially displaying an animated avatar’s face expressing a predefined emotion. Users are then asked to mimic the avatar’s expression. During this process, a custom emotion recognition model analyzes the user’s facial expressions and provides feedback on the accuracy of their projection. A small experimental study involving 8 participants tested the system for feasibility, with avatars projecting the six basic emotions and a neutral expression. The study results indicated a positive correlation between the projected facial expressions and the emotions identified by participants. Participants effectively recognized the emotions, with 85.40% accuracy demonstrating the system’s potential in enhancing the well-being of individuals. The participants were also able to mimic the given expression effectively with an accuracy of 46.67%. However, a deficiency in the performance of one of the expressions, surprise, was noticed. In the post processing, this issue was addressed and model enhancements were tailored to boost the performance by ~ 30%. This approach shows promise for therapeutic use and emotional skill development. A further wider experimental study is still required to validate the findings of this study and analyze the impact of modifications made.