Explainable Convolutional Neural Networks for Brain Cancer Detection and Localisation.

Brain cancer is widely recognised as one of the most aggressive types of tumors. In fact, approximately 70% of patients diagnosed with this malignant cancer do not survive. In this paper, we propose a method aimed to detect and localise brain cancer, starting from the analysis of magnetic resonance images. The proposed method exploits deep learning, in particular convolutional neural networks and class activation mapping, in order to provide explainability by highlighting the areas of the medical image related to brain cancer (from the model point of view). We evaluate the proposed method with 3000 magnetic resonances using a free available dataset. The results we obtained are encouraging. We reach an accuracy ranging from 97.83% to 99.67% in brain cancer detection by exploiting four different models: VGG16, ResNet50, Alex_Net, and MobileNet, thus showing the effectiveness of the proposed method.

Agreement between Optoelectronic System and Wearable Sensors for the Evaluation of Gait Spatiotemporal Parameters in Progressive Supranuclear Palsy.

The use of wearable sensors for calculating gait parameters has become increasingly popular as an alternative to optoelectronic systems, currently recognized as the gold standard. The objective of the study was to evaluate the agreement between the wearable Opal system and the optoelectronic BTS SMART DX system for assessing spatiotemporal gait parameters. Fifteen subjects with progressive supranuclear palsy walked at their self-selected speed on a straight path, and six spatiotemporal parameters were compared between the two measurement systems. The agreement was carried out through paired data test, Passing Bablok regression, and Bland-Altman Analysis. The results showed a perfect agreement for speed, a very close agreement for cadence and cycle duration, while, in the other cases, Opal system either under- or over-estimated the measurement of the BTS system. Some suggestions about these misalignments are proposed in the paper, considering that Opal system is widely used in the clinical context.

Statistical Analysis and Kinematic Assessment of Upper Limb Reaching Task in Parkinson's Disease.

The impact of neurodegenerative disorders is twofold; they affect both quality of life and healthcare expenditure. In the case of Parkinson's disease, several strategies have been attempted to support the pharmacological treatment with rehabilitation protocols aimed at restoring motor function. In this scenario, the study of upper limb control mechanisms is particularly relevant due to the complexity of the joints involved in the movement of the arm. For these reasons, it is difficult to define proper indicators of the rehabilitation outcome. In this work, we propose a methodology to analyze and extract an ensemble of kinematic parameters from signals acquired during a complex upper limb reaching task. The methodology is tested in both healthy subjects and Parkinson's disease patients (N = 12), and a statistical analysis is carried out to establish the value of the extracted kinematic features in distinguishing between the two groups under study. The parameters with the greatest number of significances across the submovements are duration, mean velocity, maximum velocity, maximum acceleration, and smoothness. Results allowed the identification of a subset of significant kinematic parameters that could serve as a proof-of-concept for a future definition of potential indicators of the rehabilitation outcome in Parkinson's disease.

Head-to-head comparison of diagnostic accuracy of stress-only myocardial perfusion imaging with conventional and cadmium-zinc telluride single-photon emission computed tomography in women with suspected coronary artery disease.

BACKGROUND: Breast attenuation may impact the diagnostic accuracy of stress myocardial perfusion imaging (MPI) with single-photon emission computed tomography (SPECT). We compared the performance of conventional (C)-SPECT and cadmium-zinc-telluride (CZT)-SPECT systems in women with low-intermediate likelihood of coronary artery disease (CAD). METHODS AND RESULTS: A total of 109 consecutive women underwent stress-optional rest MPI by both C-SPECT and CZT-SPECT. In the overall study population, a weak albeit significant correlation between total perfusion defect (TPD) measured by C-SPECT and CZT-SPECT was observed (r = 0.38, P < .001) and at Bland-Altman analysis the mean difference in TPD (C-SPECT minus CZT-SPECT) was 2.40% (P < .001). Overall concordance of semi-quantitative diagnostic performance between C-SPECT and CZT-SPECT was observed in 52 (48%) women with a κ value of 0.09. Normalcy rate was significantly higher using CZT-SPECT compared to C-SPECT (P < .001). Machine learning analysis performed through the implementation of J48 algorithm proved that CZT-SPECT has higher sensitivity, specificity, and accuracy than C-SPECT. CONCLUSIONS: In women with low-intermediate likelihood of CAD, there is a poor concordance of diagnostic performance between C-SPECT and CZT-SPECT, and CZT-SPECT allows better normalcy rate detection compared to C-SPECT.

The Kubic FLOTAC microscope (KFM): a new compact digital microscope for helminth egg counts.

The Kubic FLOTAC microscope (KFM) is a compact, low-cost, versatile and portable digital microscope designed to analyse fecal specimens prepared with Mini-FLOTAC or FLOTAC, in both field and laboratory settings. In this paper, we present the characteristics of the KFM along with its first validation for fecal egg count (FEC) of gastrointestinal nematodes (GINs) in cattle. For this latter purpose, a study was performed on 30 fecal samples from cattle experimentally infected by GINs to compare the performance of Mini-FLOTAC either using a traditional optical microscope (OM) or the KFM. The results of the comparison showed a substantial agreement (concordance correlation coefficient = 0.999), with a very low discrepancy (−0.425 ± 7.370) between the two microscopes. Moreover, the KFM captured images comparable with the view provided by the traditional OM. Therefore, the combination of sensitive, accurate, precise and standardized FEC techniques, as the Mini-FLOTAC, with a reliable automated system, will permit the real-time observation and quantification of parasitic structures, thanks also to artificial intelligence software, that is under development. For these reasons, the KFM is a promising tool for an accurate and efficient FEC to improve parasite diagnosis and to assist new generations of operators in veterinary and public health.

Toward a priori noise characterization for real-time edge-aware denoising in fluoroscopic devices.

BACKGROUND: Low-dose X-ray images have become increasingly popular in the last decades, due to the need to guarantee the lowest reasonable patient's exposure. Dose reduction causes a substantial increase of quantum noise, which needs to be suitably suppressed. In particular, real-time denoising is required to support common interventional fluoroscopy procedures. The knowledge of noise statistics provides precious information that helps to improve denoising performances, thus making noise estimation a crucial task for effective denoising strategies. Noise statistics depend on different factors, but are mainly influenced by the X-ray tube settings, which may vary even within the same procedure. This complicates real-time denoising, because noise estimation should be repeated after any changes in tube settings, which would be hardly feasible in practice. This work investigates the feasibility of an a priori characterization of noise for a single fluoroscopic device, which would obviate the need for inferring noise statics prior to each new images acquisition. The noise estimation algorithm used in this study was tested in silico to assess its accuracy and reliability. Then, real sequences were acquired by imaging two different X-ray phantoms via a commercial fluoroscopic device at various X-ray tube settings. Finally, noise estimation was performed to assess the matching of noise statistics inferred from two different sequences, acquired independently in the same operating conditions. RESULTS: The noise estimation algorithm proved capable of retrieving noise statistics, regardless of the particular imaged scene, also achieving good results even by using only 10 frames (mean percentage error lower than 2%). The tests performed on the real fluoroscopic sequences confirmed that the estimated noise statistics are independent of the particular informational content of the scene from which they have been inferred, as they turned out to be consistent in sequences of the two different phantoms acquired independently with the same X-ray tube settings. CONCLUSIONS: The encouraging results suggest that an a priori characterization of noise for a single fluoroscopic device is feasible and could improve the actual implementation of real-time denoising strategies that take advantage of noise statistics to improve the trade-off between noise reduction and details preservation.

WCTECGdb: A 12-Lead Electrocardiography Dataset Recorded Simultaneously with Raw Exploring Electrodes' Potential Directly Referred to the Right Leg.

With this paper we communicated the existence of a surface electrocardiography (ECG) recordings dataset, named WCTECGdb, that aside from the standard 12-lead signals includes the raw electrode biopotential for each of the nine exploring electrodes refereed directly to the right leg. This dataset, comprises of 540 ten second segments recorded from 92 patients at Campbelltown Hospital, NSW Australia, and is now available for download from the Physionet platform. The data included in the dataset confirm that the Wilson's Central Terminal (WCT) has a relatively large amplitude (up to 247% of lead II) with standard ECG characteristics such as a p-wave and a t-wave, and is highly variable during the cardiac cycle. As further examples of application for our data, we assess: (1) the presence of a conductive pathway between the legs and the heart concluding that in some cases is electrically significant and (2) the initial assumption about the limbs potential stating the dominance of the left arm concluding that this is not always the case and that might requires case to case assessment.

[Effects of the school backpack on walking kinematics: a mechanical overload potentially causing musculoskeletal disorders in developmental age?] / Effetti sulla cinematica del cammino dello zaino scolastico: un sovraccarico meccanico potenzialmente causa di disturbi muscolo-scheletrici in età evolutiva?

SUMMARY: Studies and reviews show that the vast majority of students around the world use heavy and uncomfortable backpacks, which could negatively affect their musculoskeletal development or at least generate a non-physiological functional overload. In this regard, non-invasive analyses were carried out on a sample of 150 healthy students aged between 14 and 15 years using a wearable inertial device for gait analysis: G-Walk System by BTS Bioengineering. Each student performed a gait analysis session consisting in a walk of 15 meters along a straight path in two different conditions: free walk and walk with backpack. A backpack with a sturdy backrest, wide and padded straps and abdominal belt with buckle was chosen. The weight inside the backpack was fixed at 9.3 kg in accordance with scientific studies conducted by Stefano Negrini of ISICO (Istituto Scientifico Italiano Colonna Vertebrale). Aim of this work is to understand, through an accurate analysis both instrumental and statistical, if we can talk about differential influence of musculoskeletal type generated by a school backpack full load compared to no backpack, trying to find out if and how much this affects walking both in terms of space-time parameters and detachment from normality values, and in terms of kinematic parameters such as pelvic rotations angles. Results showed a statistically significant difference between the space-time parameters computed in the two different study conditions, moreover a qualitative and quantitative difference was found for kinematic parameters too, which could imply potential musculoskeletal disorders associated with prolonged and long-lasting use of heavy and uncomfortable backpacks. This study has the ambition to raise awareness of this issue in order to extend legislative limits to the "working" environment of children, that is the school, as it is done for working environments adults (D. lgs 81/08 related to manual maintenance of loads).

Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation.

Upper limb amputation is a condition that significantly restricts the amputees from performing their daily activities. The myoelectric prosthesis, using signals from residual stump muscles, is aimed at restoring the function of such lost limbs seamlessly. Unfortunately, the acquisition and use of such myosignals are cumbersome and complicated. Furthermore, once acquired, it usually requires heavy computational power to turn it into a user control signal. Its transition to a practical prosthesis solution is still being challenged by various factors particularly those related to the fact that each amputee has different mobility, muscle contraction forces, limb positional variations and electrode placements. Thus, a solution that can adapt or otherwise tailor itself to each individual is required for maximum utility across amputees. Modified machine learning schemes for pattern recognition have the potential to significantly reduce the factors (movement of users and contraction of the muscle) affecting the traditional electromyography (EMG)-pattern recognition methods. Although recent developments of intelligent pattern recognition techniques could discriminate multiple degrees of freedom with high-level accuracy, their efficiency level was less accessible and revealed in real-world (amputee) applications. This review paper examined the suitability of upper limb prosthesis (ULP) inventions in the healthcare sector from their technical control perspective. More focus was given to the review of real-world applications and the use of pattern recognition control on amputees. We first reviewed the overall structure of pattern recognition schemes for myo-control prosthetic systems and then discussed their real-time use on amputee upper limbs. Finally, we concluded the paper with a discussion of the existing challenges and future research recommendations.

Fully Open-Access Passive Dry Electrodes BIOADC: Open-Electroencephalography (EEG) Re-Invented.

The Open-electroencephalography (EEG) framework is a popular platform to enable EEG measurements and general purposes Brain Computer Interface experimentations. However, the current platform is limited by the number of available channels and electrode compatibility. In this paper we present a fully configurable platform with up to 32 EEG channels and compatibility with virtually any kind of passive electrodes including textile, rubber and contactless electrodes. Together with the full hardware details, results and performance on a single volunteer participant (limited to alpha wave elicitation), we present the brain computer interface (BCI)2000 EEG source driver together with source code as well as the compiled (.exe). In addition, all the necessary device firmware, gerbers and bill of materials for the full reproducibility of the presented hardware is included. Furthermore, the end user can vary the dry-electrode reference circuitry, circuit bandwidth as well as sample rate to adapt the device to other generalized biopotential measurements. Although, not implemented in the tested prototype, the Biomedical Analogue to Digital Converter BIOADC naturally supports SPI communication for an additional 32 channels including the gain controller. In the appendix we describe the necessary modification to the presented hardware to enable this function.

Lean thinking to improve emergency department throughput at AORN Cardarelli hospital.

BACKGROUND: Throughout the world, emergency departments (ED) are characterized by overcrowding and excessive waiting times. Furthermore, the related delays significantly increase patient mortality and make inefficient use of resources to the detriment of the satisfaction of employees and patients. In this work, lean thinking is applied to the ED of Cardarelli Hospital of Naples with the aim of increasing patient flow, improving the processes that contribute to facilitating the flow of patients through the various stages of medical treatment and eliminating all bottlenecks (queue) as well as all activities that generate waste. METHODS: This project was performed at National Hospital A.O.R.N. A. Cardarelli of Naples. The historical times of access to the ED were analysed from January 2015 to June 2015, for a total of 16,563 records. Subsequently, starting in November 2015, corrective actions were implemented according to the Lean Approach. Data collected after the introduced improvements were collected from April 2016 to June 2016 and compared to those collected during the starting period. RESULTS: The results acquired before application of the Lean Thinking strategy illustrated the as-is process with its drawbacks. An analysis of the non-added value activities was performed to identify the procedures that need to be improved. After implementation of the corrective actions, we observed a positive increase in the performance of the ED, quantified as percentages of hospitalized patients according to triage codes and waiting times. CONCLUSION: This work demonstrates the applicability of Lean Thinking to ED processes and its effectiveness in terms of increasing the efficiency of services and reducing waste (waiting times).

On the Einthoven Triangle: A Critical Analysis of the Single Rotating Dipole Hypothesis.

Since its inception, electrocardiography has been based on the simplifying hypothesis that cardinal limb leads form an equilateral triangle of which, at the center/centroid, the electrical equivalent of the cardiac activity rotates during the cardiac cycle. Therefore, it is thought that the three limbs (right arm, left arm, and left leg) which enclose the heart into a circuit, where each branch directly implies current circulation through the heart, can be averaged together to form a stationary reference (central terminal) for precordials/chest-leads. Our hypothesis is that cardinal limbs do not form a triangle for the majority of the duration of the cardiac cycle. As a corollary, the central point may not lie in the plane identified by the limb leads. Using a simple and efficient algorithm, we demonstrate that the portion of the cardiac cycle where the three limb leads form a triangle is, on average less, than 50%.

Validity and reliability of textile system Sensoria for posturographic measurements.

OBJECTIVES: Smart fabrics and interactive textiles are a relatively new area of research, with many potential applications in the field of biomedical engineering. The ability of smart textiles to interact with the body provides a novel means to sense the wearer's physiology and respond to the needs of the wearer. Physiological signals, such as heart rate, breathing rates, and activity levels, are useful indicators of health status. These signals can be measured by means of textile-based sensors integrated into smart clothing which has the ability to keep a digital record of the patient's physiological responses since his or her last clinical visit, allowing doctors to make a more accurate diagnosis. Similarly, in rehabilitation, it is difficult for therapists to ensure that patients are complying with prescribed exercises. Smart garments sensing body movements have the potential to guide wearers through their exercises, while also recording their individual movements and adherence to their prescribed programme. METHODS: In this paper, we present the new wireless textile system Sensoria, with pressure sensing capability for static posturography. The gold standard for static posturography is currently the use of a pressure or force plate but, due to their very complexity and expensiveness, the applicability outside laboratories is extremely limited. RESULTS: This paper focuses on the agreement between the static computed posturography assessed by means of a traditional stabilometric platform and the Sensoria system, in twenty subjects with Parkinson's Disease (PD). CONCLUSIONS: Preliminary results showed a significant agreement between the two methods, suggesting a clinical use of Sensoria for low cost home care based balance impairment assessments.

Analysis of reaching movements of upper arm in robot assisted exercises. Kinematic assessment of robot assisted upper arm reaching single-joint movements.

Robot-mediated therapy (RMT) has been a very dynamic area of research in recent years. Robotics devices are in fact capable to quantify the performances of a rehabilitation task in treatments of several disorders of the arm and the shoulder of various central and peripheral etiology. Different systems for robot-aided neuro-rehabilitation are available for upper limb rehabilitation but the biomechanical parameters proposed until today, to evaluate the quality of the movement, are related to the specific robot used and to the type of exercise performed. Besides, none study indicated a standardized quantitative evaluation of robot assisted upper arm reaching movements, so the RMT is still far to be considered a standardised tool. In this paper a quantitative kinematic assessment of robot assisted upper arm reaching movements, considering also the effect of gravity on the quality of the movements, is proposed. We studied a group of 10 healthy subjects and results indicate that our advised protocol can be useful for characterising normal pattern in reaching movements.

Individual identification via electrocardiogram analysis.

BACKGROUND: During last decade the use of ECG recordings in biometric recognition studies has increased. ECG characteristics made it suitable for subject identification: it is unique, present in all living individuals, and hard to forge. However, in spite of the great number of approaches found in literature, no agreement exists on the most appropriate methodology. This study aimed at providing a survey of the techniques used so far in ECG-based human identification. Specifically, a pattern recognition perspective is here proposed providing a unifying framework to appreciate previous studies and, hopefully, guide future research. METHODS: We searched for papers on the subject from the earliest available date using relevant electronic databases (Medline, IEEEXplore, Scopus, and Web of Knowledge). The following terms were used in different combinations: electrocardiogram, ECG, human identification, biometric, authentication and individual variability. The electronic sources were last searched on 1st March 2015. In our selection we included published research on peer-reviewed journals, books chapters and conferences proceedings. The search was performed for English language documents. RESULTS: 100 pertinent papers were found. Number of subjects involved in the journal studies ranges from 10 to 502, age from 16 to 86, male and female subjects are generally present. Number of analysed leads varies as well as the recording conditions. Identification performance differs widely as well as verification rate. Many studies refer to publicly available databases (Physionet ECG databases repository) while others rely on proprietary recordings making difficult them to compare. As a measure of overall accuracy we computed a weighted average of the identification rate and equal error rate in authentication scenarios. Identification rate resulted equal to 94.95 % while the equal error rate equal to 0.92 %. CONCLUSIONS: Biometric recognition is a mature field of research. Nevertheless, the use of physiological signals features, such as the ECG traits, needs further improvements. ECG features have the potential to be used in daily activities such as access control and patient handling as well as in wearable electronics applications. However, some barriers still limit its growth. Further analysis should be addressed on the use of single lead recordings and the study of features which are not dependent on the recording sites (e.g. fingers, hand palms). Moreover, it is expected that new techniques will be developed using fiducials and non-fiducial based features in order to catch the best of both approaches. ECG recognition in pathological subjects is also worth of additional investigations.

Telemedicine supported by Augmented Reality: an interactive guide for untrained people in performing an ECG test.

BACKGROUND: In many telemedicine applications, the correct use of medical device at the point of need is essential to provide an appropriate service. Some applications may require untrained people to interact with medical devices and patients: care delivery in transportation, military actions, home care and telemedicine training.Appropriate operation of medical device and correct connection with patient's body are crucial. In these scenarios, tailored applications of Augmented Reality can offer a valid support by guiding untrained people at the point of need. This study aims to explore the feasibility of using Augmented Reality in telemedicine applications, by facilitating an acceptable use of biomedical equipment by any unskilled person. In particular, a prototype system was built in order to estimate how untrained users, with limited or no knowledge, can effectively interact with an ECG device and properly placing ECG electrodes on patient's chest. METHODS: An Augmented Reality application was built to support untrained users in performing an ECG test. Simple markers attached to the ECG device and onto patient's thorax allow camera calibration. Once objects and their pose in the space are recognized, the video of the current scene is enriched, in real-time, with additional pointers, text boxes and audio that help the untrained operator to perform the appropriate sequence of operations. All the buttons, switches, ports of the ECG device together with the location of precordial leads were coded and indicated. Some user's voice commands were also included to improve usability. RESULTS: Ten untrained volunteers, supported by the augmented reality, were able to carry out a complete ECG test first on a mannequin and then on a real patient in a reasonable time (about 8 minutes on average). Average positioning errors of precordial electrodes resulted less than 3 mm for the mannequin and less than 7 mm for the real patient. These preliminary findings suggest the effectiveness of the developed application and the validity of clinical ECG recordings. CONCLUSION: This application can be adapted to support the use of other medical equipment as well as other telemedicine tasks and it could be performed with a Tablet or a Smartphone.

Colon cancer diagnosis by means of explainable deep learning.

Early detection of the adenocarcinoma cancer in colon tissue by means of explainable deep learning, by classifying histological images and providing visual explainability on model prediction. Considering that in recent years, deep learning techniques have emerged as powerful techniques in medical image analysis, offering unprecedented accuracy and efficiency, in this paper we propose a method to automatically detect the presence of cancerous cells in colon tissue images. Various deep learning architectures are considered, with the aim of considering the best one in terms of quantitative and qualitative results. As a matter of fact, we consider qualitative results by taking into account the so-called prediction explainability, by providing a way to highlight on the tissue images the areas that from the model point of view are related to the presence of colon cancer. The experimental analysis, performed on 10,000 colon issue images, showed the effectiveness of the proposed method by obtaining an accuracy equal to 0.99. The experimental analysis shows that the proposed method can be successfully exploited for colon cancer detection and localisation from tissue images.

Alzheimer's Disease Evaluation Through Visual Explainability by Means of Convolutional Neural Networks.

Background and Objective: Alzheimer's disease is nowadays the most common cause of dementia. It is a degenerative neurological pathology affecting the brain, progressively leading the patient to a state of total dependence, thus creating a very complex and difficult situation for the family that has to assist him/her. Early diagnosis is a primary objective and constitutes the hope of being able to intervene in the development phase of the disease. Methods: In this paper, a method to automatically detect the presence of Alzheimer's disease, by exploiting deep learning, is proposed. Five different convolutional neural networks are considered: ALEX_NET, VGG16, FAB_CONVNET, STANDARD_CNN and FCNN. The first two networks are state-of-the-art models, while the last three are designed by authors. We classify brain images into one of the following classes: non-demented, very mild demented and mild demented. Moreover, we highlight on the image the areas symptomatic of Alzheimer presence, thus providing a visual explanation behind the model diagnosis. Results: The experimental analysis, conducted on more than 6000 magnetic resonance images, demonstrated the effectiveness of the proposed neural networks in the comparison with the state-of-the-art models in Alzheimer's disease diagnosis and localization. The best results in terms of metrics are the best with STANDARD_CNN and FCNN with accuracy, precision and recall between 98% and 95%. Excellent results also from a qualitative point of view are obtained with the Grad-CAM for localization and visual explainability. Conclusions: The analysis of the heatmaps produced by the Grad-CAM algorithm shows that in almost all cases the heatmaps highlight regions such as ventricles and cerebral cortex. Future work will focus on the realization of a network capable of analyzing the three anatomical views simultaneously.

Capability of Machine Learning Algorithms to Classify Safe and Unsafe Postures during Weight Lifting Tasks Using Inertial Sensors.

Occupational ergonomics aims to optimize the work environment and to enhance both productivity and worker well-being. Work-related exposure assessment, such as lifting loads, is a crucial aspect of this discipline, as it involves the evaluation of physical stressors and their impact on workers' health and safety, in order to prevent the development of musculoskeletal pathologies. In this study, we explore the feasibility of machine learning (ML) algorithms, fed with time- and frequency-domain features extracted from inertial signals (linear acceleration and angular velocity), to automatically and accurately discriminate safe and unsafe postures during weight lifting tasks. The signals were acquired by means of one inertial measurement unit (IMU) placed on the sternums of 15 subjects, and subsequently segmented to extract several time- and frequency-domain features. A supervised dataset, including the extracted features, was used to feed several ML models and to assess their prediction power. Interesting results in terms of evaluation metrics for a binary safe/unsafe posture classification were obtained with the logistic regression algorithm, which outperformed the others, with accuracy and area under the receiver operating characteristic curve values of up to 96% and 99%, respectively. This result indicates the feasibility of the proposed methodology-based on a single inertial sensor and artificial intelligence-to discriminate safe/unsafe postures associated with load lifting activities. Future investigation in a wider study population and using additional lifting scenarios could confirm the potentiality of the proposed methodology, supporting its applicability in the occupational ergonomics field.

Investigating the role of capacitive coupling between the operating table and the return electrode of an electrosurgery unit in the modification of the current density distribution within the patients' body.

BACKGROUND: Electrosurgery units are widely employed in modern surgery. Advances in technology have enhanced the safety of these devices, nevertheless, accidental burns are still regularly reported. This study focuses on possible causes of sacral burns as complication of the use of electrosurgery. Burns are caused by local densifications of the current, but the actual pathway of current within patient's body is unknown. Numerical electromagnetic analysis can help in understanding the issue. METHODS: To this aim, an accurate heterogeneous model of human body (including seventy-seven different tissues), electrosurgery electrodes, operating table and mattress was build to resemble a typical surgery condition. The patient lays supine on the mattress with the active electrode placed onto the thorax and the return electrode on his back. Common operating frequencies of electrosurgery units were considered. Finite Difference Time Domain electromagnetic analysis was carried out to compute the spatial distribution of current density within the patient's body. A differential analysis by changing the electrical properties of the operating table from a conductor to an insulator was also performed. RESULTS: Results revealed that distributed capacitive coupling between patient body and the conductive operating table offers an alternative path to the electrosurgery current. The patient's anatomy, the positioning and the different electromagnetic properties of tissues promote a densification of the current at the head and sacral region. In particular, high values of current density were located behind the sacral bone and beneath the skin. This did not occur in the case of non-conductive operating table. CONCLUSION: Results of the simulation highlight the role played from capacitive couplings between the return electrode and the conductive operating table. The concentration of current density may result in an undesired rise in temperature, originating burns in body region far from the electrodes. This outcome is concordant with the type of surgery-related sacral burns reported in literature. Such burns cannot be immediately detected after surgery, but appear later and can be confused with bedsores. In addition, the dosimetric analysis suggests that reducing the capacity coupling between the return electrode and the operating table can decrease or avoid this problem.