A Real-Time, Automatic, and Dynamic Scheduling and Control System for PET Patients Based on Wearable Sensors.

Positron emission tomography (PET) is one of the commonly used scanning techniques. Medical staff manually calculate the estimated scan time for each PET device. However, the number of PET scanning devices is small, the number of patients is large, and there are many changes including rescanning requirements, which makes it very error-prone, puts pressure on staff, and causes trouble for patients and their families. Although previous studies proposed algorithms for specific inspections, there is currently no research on improving the PET process. This paper proposes a real-time automatic scheduling and control system for PET patients with wearable sensors. The system can automatically schedule, estimate and instantly update the time of various tasks, and automatically allocate beds and announce schedule information in real time. We implemented this system, collected time data of 200 actual patients, and put these data into the implementation program for simulation and comparison. The average time difference between manual and automatic scheduling was 7.32 min, and it could reduce the average examination time of 82% of patients by 6.14 ± 4.61 min. This convinces us the system is correct and can improve time efficiency, while avoiding human error and staff pressure, and avoiding trouble for patients and their families.

Improvement of a Privacy Authentication Scheme Based on Cloud for Medical Environment.

Medical systems allow patients to receive care at different hospitals. However, this entails considerable inconvenience through the need to transport patients and their medical records between hospitals. The development of Telecare Medicine Information Systems (TMIS) makes it easier for patients to seek medical treatment and to store and access medical records. However, medical data stored in TMIS is not encrypted, leaving patients' private data vulnerable to external leaks. In 2014, scholars proposed a new cloud-based medical information model and authentication scheme which would not only allow patients to remotely access medical services but also protects patient privacy. However, this scheme still fails to provide patient anonymity and message authentication. Furthermore, this scheme only stores patient medical data, without allowing patients to directly access medical advice. Therefore, we propose a new authentication scheme, which provides anonymity, unlinkability, and message authentication, and allows patients to directly and remotely consult with doctors. In addition, our proposed scheme is more efficient in terms of computation cost. The proposed system was implemented in Android system to demonstrate its workability.

Modifying the ECC-based grouping-proof RFID system to increase inpatient medication safety.

RFID technology is increasingly used in applications that require tracking, identification, and authentication. It attaches RFID-readable tags to objects for identification and execution of specific RFID-enabled applications. Recently, research has focused on the use of grouping-proofs for preserving privacy in RFID applications, wherein a proof of two or more tags must be simultaneously scanned. In 2010, a privacy-preserving grouping proof protocol for RFID based on ECC in public-key cryptosystem was proposed but was shown to be vulnerable to tracking attacks. A proposed enhancement protocol was also shown to have defects which prevented proper execution. In 2012, Lin et al. proposed a more efficient RFID ECC-based grouping proof protocol to promote inpatient medication safety. However, we found this protocol is also vulnerable to tracking and impersonation attacks. We then propose a secure privacy-preserving RFID grouping proof protocol for inpatient medication safety and demonstrate its resistance to such attacks.

Augmented Reality Surgical Navigation System Integrated with Deep Learning.

Most current surgical navigation methods rely on optical navigators with images displayed on an external screen. However, minimizing distractions during surgery is critical and the spatial information displayed in this arrangement is non-intuitive. Previous studies have proposed combining optical navigation systems with augmented reality (AR) to provide surgeons with intuitive imaging during surgery, through the use of planar and three-dimensional imagery. However, these studies have mainly focused on visual aids and have paid relatively little attention to real surgical guidance aids. Moreover, the use of augmented reality reduces system stability and accuracy, and optical navigation systems are costly. Therefore, this paper proposed an augmented reality surgical navigation system based on image positioning that achieves the desired system advantages with low cost, high stability, and high accuracy. This system also provides intuitive guidance for the surgical target point, entry point, and trajectory. Once the surgeon uses the navigation stick to indicate the position of the surgical entry point, the connection between the surgical target and the surgical entry point is immediately displayed on the AR device (tablet or HoloLens glasses), and a dynamic auxiliary line is shown to assist with incision angle and depth. Clinical trials were conducted for EVD (extra-ventricular drainage) surgery, and surgeons confirmed the system's overall benefit. A "virtual object automatic scanning" method is proposed to achieve a high accuracy of 1 ± 0.1 mm for the AR-based system. Furthermore, a deep learning-based U-Net segmentation network is incorporated to enable automatic identification of the hydrocephalus location by the system. The system achieves improved recognition accuracy, sensitivity, and specificity of 99.93%, 93.85%, and 95.73%, respectively, representing a significant improvement from previous studies.

Augmented Reality Surgical Navigation System for External Ventricular Drain.

Augmented reality surgery systems are playing an increasing role in the operating room, but applying such systems to neurosurgery presents particular challenges. In addition to using augmented reality technology to display the position of the surgical target position in 3D in real time, the application must also display the scalpel entry point and scalpel orientation, with accurate superposition on the patient. To improve the intuitiveness, efficiency, and accuracy of extra-ventricular drain surgery, this paper proposes an augmented reality surgical navigation system which accurately superimposes the surgical target position, scalpel entry point, and scalpel direction on a patient's head and displays this data on a tablet. The accuracy of the optical measurement system (NDI Polaris Vicra) was first independently tested, and then complemented by the design of functions to help the surgeon quickly identify the surgical target position and determine the preferred entry point. A tablet PC was used to display the superimposed images of the surgical target, entry point, and scalpel on top of the patient, allowing for correct scalpel orientation. Digital imaging and communications in medicine (DICOM) results for the patient's computed tomography were used to create a phantom and its associated AR model. This model was then imported into the application, which was then executed on the tablet. In the preoperative phase, the technician first spent 5-7 min to superimpose the virtual image of the head and the scalpel. The surgeon then took 2 min to identify the intended target position and entry point position on the tablet, which then dynamically displayed the superimposed image of the head, target position, entry point position, and scalpel (including the scalpel tip and scalpel orientation). Multiple experiments were successfully conducted on the phantom, along with six practical trials of clinical neurosurgical EVD. In the 2D-plane-superposition model, the optical measurement system (NDI Polaris Vicra) provided highly accurate visualization (2.01 ± 1.12 mm). In hospital-based clinical trials, the average technician preparation time was 6 min, while the surgeon required an average of 3.5 min to set the target and entry-point positions and accurately overlay the orientation with an NDI surgical stick. In the preparation phase, the average time required for the DICOM-formatted image processing and program import was 120 ± 30 min. The accuracy of the designed augmented reality optical surgical navigation system met clinical requirements, and can provide a visual and intuitive guide for neurosurgeons. The surgeon can use the tablet application to obtain real-time DICOM-formatted images of the patient, change the position of the surgical entry point, and instantly obtain an updated surgical path and surgical angle. The proposed design can be used as the basis for various augmented reality brain surgery navigation systems in the future.

Secure method for biometric-based recognition with integrated cryptographic functions.

Biometric systems refer to biometric technologies which can be used to achieve authentication. Unlike cryptography-based technologies, the ratio for certification in biometric systems needs not to achieve 100% accuracy. However, biometric data can only be directly compared through proximal access to the scanning device and cannot be combined with cryptographic techniques. Moreover, repeated use, improper storage, or transmission leaks may compromise security. Prior studies have attempted to combine cryptography and biometrics, but these methods require the synchronization of internal systems and are vulnerable to power analysis attacks, fault-based cryptanalysis, and replay attacks. This paper presents a new secure cryptographic authentication method using biometric features. The proposed system combines the advantages of biometric identification and cryptographic techniques. By adding a subsystem to existing biometric recognition systems, we can simultaneously achieve the security of cryptographic technology and the error tolerance of biometric recognition. This method can be used for biometric data encryption, signatures, and other types of cryptographic computation. The method offers a high degree of security with protection against power analysis attacks, fault-based cryptanalysis, and replay attacks. Moreover, it can be used to improve the confidentiality of biological data storage and biodata identification processes. Remote biometric authentication can also be safely applied.