Lightweight Multi-Class Support Vector Machine-Based Medical Diagnosis System with Privacy Preservation.

Machine learning, powered by cloud servers, has found application in medical diagnosis, enhancing the capabilities of smart healthcare services. Research literature demonstrates that the support vector machine (SVM) consistently demonstrates remarkable accuracy in medical diagnosis. Nonetheless, safeguarding patients' health data privacy and preserving the intellectual property of diagnosis models is of paramount importance. This concern arises from the common practice of outsourcing these models to third-party cloud servers that may not be entirely trustworthy. Few studies in the literature have delved into addressing these issues within SVM-based diagnosis systems. These studies, however, typically demand substantial communication and computational resources and may fail to conceal classification results and protect model intellectual property. This paper aims to tackle these limitations within a multi-class SVM medical diagnosis system. To achieve this, we have introduced modifications to an inner product encryption cryptosystem and incorporated it into our medical diagnosis framework. Notably, our cryptosystem proves to be more efficient than the Paillier and multi-party computation cryptography methods employed in previous research. Although we focus on a medical application in this paper, our approach can also be used for other applications that need the evaluation of machine learning models in a privacy-preserving way such as electricity theft detection in the smart grid, electric vehicle charging coordination, and vehicular social networks. To assess the performance and security of our approach, we conducted comprehensive analyses and experiments. Our findings demonstrate that our proposed method successfully fulfills our security and privacy objectives while maintaining high classification accuracy and minimizing communication and computational overhead.

Preliminary design and evaluation of a remote tele-mentoring system for minimally invasive surgery.

BACKGROUND: Tele-mentoring during surgery facilitates the transfer of surgical knowledge from a mentor (specialist surgeon) to a mentee (operating surgeon). The aim of this work is to develop a tele-mentoring system tailored for minimally invasive surgery (MIS) where the mentor can remotely demonstrate to the mentee the required motion of the surgical instruments. METHODS: A remote tele-mentoring system is implemented that generates visual cues in the form of virtual surgical instrument motion overlaid onto the live view of the operative field. The technical performance of the system is evaluated in a simulated environment, where the operating room and the central location of the mentor were physically located in different countries and connected over the internet. In addition, a user study was performed to assess the system as a mentoring tool. RESULTS: On average, it took 260 ms to send a view of the operative field of 1920 × 1080 resolution from the operating room to the central location of the mentor and an average of 132 ms to receive the motion of virtual surgical instruments from the central location to the operating room. The user study showed that it is feasible for the mentor to demonstrate and for the mentee to understand and replicate the motion of surgical instruments. CONCLUSION: The work demonstrates the feasibility of transferring information over the internet from a mentor to a mentee in the form of virtual surgical instruments. Their motion is overlaid onto the live view of the operative field enabling real-time interactions between both the surgeons.

Q-Learning Based Joint Energy-Spectral Efficiency Optimization in Multi-Hop Device-to-Device Communication.

In scenarios, like critical public safety communication networks, On-Scene Available (OSA) user equipment (UE) may be only partially connected with the network infrastructure, e.g., due to physical damages or on-purpose deactivation by the authorities. In this work, we consider multi-hop Device-to-Device (D2D) communication in a hybrid infrastructure where OSA UEs connect to each other in a seamless manner in order to disseminate critical information to a deployed command center. The challenge that we address is to simultaneously keep the OSA UEs alive as long as possible and send the critical information to a final destination (e.g., a command center) as rapidly as possible, while considering the heterogeneous characteristics of the OSA UEs. We propose a dynamic adaptation approach based on machine learning to improve a joint energy-spectral efficiency (ESE). We apply a Q-learning scheme in a hybrid fashion (partially distributed and centralized) in learner agents (distributed OSA UEs) and scheduler agents (remote radio heads or RRHs), for which the next hop selection and RRH selection algorithms are proposed. Our simulation results show that the proposed dynamic adaptation approach outperforms the baseline system by approximately 67% in terms of joint energy-spectral efficiency, wherein the energy efficiency of the OSA UEs benefit from a gain of approximately 30%. Finally, the results show also that our proposed framework with C-RAN reduces latency by approximately 50% w.r.t. the baseline.

Development and Evaluation of a Mixed-Reality Tele-ultrasound System.

OBJECTIVE: The objective of this feasibility study was to develop and assess a tele-ultrasound system that would enable an expert sonographer (situated at the remote site) to provide real-time guidance to an operator (situated at the imaging site) using a mixed-reality environment. METHODS: An architecture along with the operational workflow of the system is designed and a prototype is developed that enables guidance in form of audiovisual cues. The visual cues comprise holograms (of the ultrasound images and ultrasound probe) and is rendered to the operator using a head-mounted display device. The position and orientation of the ultrasound probe's hologram are remotely controlled by the expert sonographer and guide the placement of a physical ultrasound probe at the imaging site. The developed prototype was evaluated for its performance on a network. In addition, a user study (with 12 participants) was conducted to assess the operator's ability to align the probe under different guidance modes. RESULTS: The network performance revealed the view of the imaging site and ultrasound images were transferred to the remote site in 233 ± 42 and 158 ± 38 ms, respectively. The expert sonographer was able to transfer, to the imaging site, data related to position and orientation of the ultrasound probe's hologram in 78 ± 13 ms. The user study indicated that the audiovisual cues are sufficient for an operator to position and orient a physical probe for accurate depiction of the targeted tissue (p < 0.001). The probe's placement translational and rotational errors were 1.4 ± 0.6 mm and 5.4 ± 2.2º. CONCLUSION: The work illustrates the feasibility of using a mixed-reality environment for effective communication between an expert sonographer (ultrasound physician) and an operator. Further studies are required to determine its applicability in a clinical setting during tele-ultrasound.

Evaluation of user-interfaces for controlling movements of virtual minimally invasive surgical instruments.

BACKGROUND: Recent tele-mentoring technologies for minimally invasive surgery (MIS) augments the operative field with movements of virtual surgical instruments as visual cues. The objective of this work is to assess different user-interfaces that effectively transfer mentor's hand gestures to the movements of virtual surgical instruments. METHODS: A user study was conducted to assess three different user-interface devices (Oculus-Rift, SpaceMouse, Touch Haptic device) under various scenarios. The devices were integrated with a MIS tele-mentoring framework for control of both manual and robotic virtual surgical instruments. RESULTS: The user study revealed that Oculus Rift is preferred during robotic scenarios, whereas the touch haptic device is more suitable during manual scenarios for tele-mentoring. CONCLUSION: A user-interface device in the form of a stylus controlled by fingers for pointing in 3D space is more suitable for manual MIS, whereas a user-interface that can be moved and oriented easily in 3D space by wrist motion is more suitable for robotic MIS.

A Novel Pandemic Tracking Map: From Theory to Implementation.

The wide spread of the novel COVID-19 virus all over the world has caused major economical and social damages combined with the death of more than two million people so far around the globe. Therefore, the design of a model that can predict the persons that are most likely to be infected is a necessity to control the spread of this infectious disease as well as any other future novel pandemic. In this paper, an Internet of Things (IoT) sensing network is designed to anonymously track the movement of individuals in crowded zones through collecting the beacons of WiFi and Bluetooth devices from mobile phones to triangulate and estimate the locations of individuals inside buildings without violating their privacy. A mathematical model is presented to compute the expected time of exposure between users. Furthermore, a virus spread mathematical model as well as iterative spread tracking algorithms are proposed to predict the probability of individuals being infected even with limited data.

Towards development of a tele-mentoring framework for minimally invasive surgeries.

BACKGROUND: Tele-mentoring facilitates the transfer of surgical knowledge. The objective of this work is to develop a tele-mentoring framework that enables a specialist surgeon to mentor an operating surgeon by transferring information in a form of surgical instruments' motion required during a minimally invasive surgery. METHOD: A tele-mentoring framework is developed to transfer video stream of the surgical field, poses of the scope and port placement from the operating room to a remote location. From the remote location, the motion of virtual surgical instruments augmented onto the surgical field is sent to the operating room. RESULTS: The proposed framework is suitable to be integrated with laparoscopic as well as robotic surgeries. It takes on average 1.56 s to send information from the operating room to the remote location and 0.089 s for vice versa over a local area network. CONCLUSIONS: The work demonstrates a tele-mentoring framework that enables a specialist surgeon to mentor an operating surgeon during a minimally invasive surgery.

A Real-Time ECG Feature Extraction Algorithm for Detecting Meditation Levels within a General Measurement Setup.

This paper presents a setup for the real-time extraction of Electroencephalography (EEG) and Electrocardiogram (ECG) features indicating the level of focus, relaxation, or meditation of a given subject. An algorithm for detecting meditation in real-time using the extracted ECG features is designed and shown to lead to accurate results using an online ECG measurement dataset. Similar methods can be used for EEG data, such that the proposed measurement setup can be used, for example, for investigating the effect of virtual reality based EEG training, with and without neurofeedback, on the capability of subjects to focus, relax, or meditate.

A multiorganism based method for Bayesian gene network estimation.

The primary goal of this article is to infer genetic interactions based on gene expression data. A new method for multiorganism Bayesian gene network estimation is presented based on multitask learning. When the input datasets are sparse, as is the case in microarray gene expression data, it becomes difficult to separate random correlations from true correlations that would lead to actual edges when modeling the gene interactions as a Bayesian network. Multitask learning takes advantage of the similarity between related tasks, in order to construct a more accurate model of the underlying relationships represented by the Bayesian networks. The proposed method is tested on synthetic data to illustrate its validity. Then it is iteratively applied on real gene expression data to learn the genetic regulatory networks of two organisms with homologous genes.