A Review on Meshing Techniques in Biomedicine

Engineering has a wide range of applications where more detailed and reliable data are needed, one of which is biomedicine. One of the aims of meshing is to use the Finite Element Approach to solve the problem. By analysing and segmenting raw medical imaging data, meshing aids in a better and more precise understanding of the organs and structures of human body. The main goal of this paper is to collect and review the various available methods in meshing. Also, a comparison study of different meshing techniques that are available in biomedicine is performed.

Empirical Study on Medical Information and Communication Technology System in Dentistry in Southeast Asia

In the field of dentistry, diagnoses based on data obtained using medical imaging modalities such as digital panoramic radiography and cone beam computed tomography (CBCT) have been widely recommended for advanced dental care. In Lao People’s Democratic Republic (Laos), there are place where advanced dental imaging devices are available in only one university dental hospital. The establishment of an information and communication technology (ICT) system has been expected as telemedicine system, for sharing medical imaging data among medical institutions in Laos. Recently, regional medical cooperation using telemedicine has been developed in Japan, and medical imaging data have been provided and shared among medical institutions, by using a mobile tablet terminal application. Therefore, we have carried out the empirical research on the telemedicine system with the university in Laos. The technologies and research results from our project will be presented in this chapter.

Augmented Reality for Retrosigmoid Craniotomy Planning

While medical imaging data have traditionally been viewed on two-dimensional (2D) displays, augmented reality (AR) allows physicians to project the medical imaging data on patient's bodies to locate important anatomy. We present a surgical AR application to plan the retrosigmoid craniotomy, a standard approach to access the posterior fossa and the internal auditory canal. As a simple and accurate alternative to surface landmarks and conventional surgical navigation systems, our AR application augments the surgeon's vision to guide the optimal location of cortical bone removal. In this work, two surgeons performed a retrosigmoid approach 14 times on eight cadaver heads. In each case, the surgeon manually aligned a computed tomography (CT)-derived virtual rendering of the sigmoid sinus on the real cadaveric heads using a see-through AR display, allowing the surgeon to plan and perform the craniotomy accordingly. Postprocedure CT scans were acquired to assess the accuracy of the retrosigmoid craniotomies with respect to their intended location relative to the dural sinuses. The two surgeons had a mean margin of davg = 0.6 ± 4.7 mm and davg = 3.7 ± 2.3 mm between the osteotomy border and the dural sinuses over all their cases, respectively, and only positive margins for 12 of the 14 cases. The intended surgical approach to the internal auditory canal was successfully achieved in all cases using the proposed method, and the relatively small and consistent margins suggest that our system has the potential to be a valuable tool to facilitate planning a variety of similar skull-base procedures.

Interoperability Reference Models for Applications of Artificial Intelligence in Medical Imaging

Medical imaging is currently being applied in artificial intelligence and big data technologies in data formats. In order for medical imaging collected from different institutions and systems to be used for artificial intelligence data, interoperability is becoming a key element. Whilst interoperability is currently guaranteed through medical data standards, compliance to personal information protection laws, and other methods, a standard solution for measurement values is deemed to be necessary in order for further applications as artificial intelligence data. As a result, this study proposes a model for interoperability in medical data standards, personal information protection methods, and medical imaging measurements. This model applies Health Level Seven (HL7) and Digital Imaging and Communications in Medicine (DICOM) standards to medical imaging data standards and enables increased accessibility towards medical imaging data in the compliance of personal information protection laws through the use of de-identifying methods. This study focuses on offering a standard for the measurement values of standard materials that addresses uncertainty in measurements that pre-existing medical imaging measurement standards did not provide. The study finds that medical imaging data standards conform to pre-existing standards and also provide protection to personal information within any medical images through de-identifying methods. Moreover, it proposes a reference model that increases interoperability by composing a process that minimizes uncertainty using standard materials. The interoperability reference model is expected to assist artificial intelligence systems using medical imaging and further enhance the resilience of future health technologies and system development.