As medical DX is progressing, digitization is also attracting attention in the field of surgery, which greatly depends on the skill and experiences of doctors. The digitization of information output from each device in the operating room has made it possible to accumulate big data. This paper outlines the smart operating room SCOT (Smart Cyber Operating Theater) and the clinical information analyzer named C.I.A. Then, we will discuss current issues and future prospects. SCOT has a network connection function between medical devices in the operating room, and applications running on it can be used for a variety of purposes, such as integrated information navigation system using time-synchronized information, analysis and surgical procedure prediction, so on. In addition, as an application of AI in neurosurgery, we will introduce an outline of the C.I.A., and pros and cons.

Artificial Intelligence (AI) is expanding its application fields. It is studied from various perspectives in medicine and even in intraoperative supports. This paper presents some interesting approaches of AIs for surgical assistance, which consists of surgical planning, intraoperative measurement, preoperative and intraoperative data registration, guidance information presentation, system operation interface, and error analysis, which are the procedures performed in surgical support.

Laparoscopic surgery is currently performed as one of surgical procedures for various organs. Laparoscopic surgery requires highly skill of surgeon. Therefore, computer aided surgery which is used computer technology for assisting surgery has been researched. These assistances are sometimes referred as surgical navigation. In recent year, as the development of AI (Artificial Intelligence) technology, there are many researches on laparoscopic video analysis using deep learning for assisting laparoscopic surgery. In this paper, we introduce our researches about laparoscopic video analysis using AI.

There has been much interest in the surgical robotics community in the use of image-based strategies for robot control. Especially in the surgical context, images are the main source of information for autonomous control. In this work, we summarize the occurrence of so-called AI-based methods in commercial and research-oriented surgical robotics systems. We discuss the current state of research in the field, open challenges, and provide some directions for further research that have been impactful in related fields.

This study simulated PSF reconstruction with edge artifacts mitigation while recovering spatial resolution under Poisson noise, and investigated the cause of contrast dependence. In 2D numerical phantoms consisted of disk 184 mm in diameter and different contrast of 7 disks 20 mm in diameter, we blurred the projection data using 1D Gaussian detector response with 5 mm FWHM. Projection matrix of parallel beam geometry considered only detector response and did not include other components so as to edge artifacts were not influenced. Reconstruction was performed using OSEM regularized with total variation (TV). To suppress noise and to perform stable deconvolution, blur functions with 9.94 mm FWHM given by the convolution of detector response 5 mm FWHM and 1D Gaussian filter with 8.59 mm FWHM was used in forward projection. FWHM of edge spread function was measured as a spatial resolution index. FWHM of PSF-TV reconstruction at projection data of 5 × 10⁷ counts decreased as 1.55, 1.29, 1.28, 1.17, 1.09, 1.11, 1.04 mm at contrast of 0.5, 1, 2, 3, 4, 5, 6, respectively. Contrast dependence of spatial resolution in the presence of noise is due to TV regularization, which acts to smooth objects with low-contrast more strongly than those with high-contrast.

In the second article of this series, we explained the method of reconstructing a refraction-contrast CT image from a refraction-contrast image obtained by X-ray Dark-Field Imaging (XDFI)．In this article, we will present images of biological specimens obtained by XDFI in recent years and the research efforts in pathological applications. In addition, refraction-contrast images of various tissues obtained in the past are described, focusing on the differences from conventional pathology images.