Real-time in situ three-dimensional integral videography and surgical navigation using augmented reality: a pilot study

To evaluate the feasibility and accuracy of a three-dimensional augmented reality system incorporating integral videography for imaging oral and maxillofacial regions, based on preoperative computed tomography data. Three-dimensional surface models of the jawbones, based on the computed tomography data, were used to create the integral videography images of a subject＇s maxillofacial area. The three-dimensional augmented reality system （integral videography display, computed tomography, a position tracker and a computer） was used to generate a three-dimensional overlay that was projected on the surgical site via a half-silvered mirror. Thereafter, a feasibility study was performed on a volunteer. The accuracy of this system was verified on a solid model while simulating bone resection. Positional registration was attained by identifying and tracking the patient/surgical instrument＇s position. Thus, integral videography images of jawbones, teeth and the surgical tool were superimposed in the correct position. Stereoscopic images viewed from various angles were accurately displayed. Change in the viewing angle did not negatively affect the surgeon＇s ability to simultaneously observe the three-dimensional images and the patient, without special glasses. The difference in three-dimensional position of each measuring point on the solid model and augmented reality navigation was almost negligible （〈1 mm）; this indicates that the system was highly accurate. This augmented reality system was highly accurate and effective for surgical navigation and for overlaying a three-dimensional computed tomography image on a patient＇s surgical area, enabling the surgeon to understand the positional relationship between the preoperative image and the actual surgical site, with the naked eye.

Screening COVID-19 from chest X-ray images by an optical diffractive neural network with the optimized F number

The COVID-19 pandemic continues to significantly impact people's lives worldwide, emphasizing the critical need for effective detection methods. Many existing deep learning-based approaches for COVID-19 detection offer high accuracy but demand substantial computing resources, time, and energy. In this study, we introduce an optical diffractive neural network(ODNN-COVID), which is characterized by low power consumption, efficient parallelization, and fast computing speed for COVID-19 detection. In addition, we explore how the physical parameters of ODNN-COVID affect its diagnostic performance. We identify the F number as a key parameter for evaluating the overall detection capabilities. Through an assessment of the connectivity of the diffractive network, we established an optimized range of F number, offering guidance for constructing optical diffractive neural networks. In the numerical simulations, a three-layer system achieves an impressive overall accuracy of 92.64% and 88.89% in binary-and threeclassification diagnostic tasks. For a single-layer system, the simulation accuracy of 84.17% and the experimental accuracy of 80.83% can be obtained with the same configuration for the binary-classification task, and the simulation accuracy is 80.19% and the experimental accuracy is 74.44% for the three-classification task. Both simulations and experiments validate that the proposed optical diffractive neural network serves as a passive optical processor for effective COVID-19 diagnosis, featuring low power consumption, high parallelization, and fast computing capabilities. Furthermore, ODNN-COVID exhibits versatility, making it adaptable to various image analysis and object classification tasks related to medical fields owing to its general architecture.

Artificial intelligence-based ultrasound imaging technologies for hepatic diseases

Ultrasound(US)imaging is a non-invasive,real-time,economical,and convenient imaging modality that has been widely used in diagnosing and treating hepatic diseases.Artificial intelligence(AI)technology can predict or make decisions based on the experience of clinical experts and knowledge obtained from training data.This technology can help clinicians improve the detection efficiency and evaluate hepatic diseases,promote clinical treatment of the liver,and predict the response of the liver after treatment.This review summarizes the current rapid development of US technology and related AI methods in the diagnosis and treatment of hepatic diseases.Covered topics include steatosis grading,fibrosis staging,detection of focal liver lesions,US image segmentation,multimodal image registration,and other applications.At present,the field of AI in US imaging is still in its early stages.With the future progress of AI technology,AI-based US imaging can further improve diagnosis,reduce medical costs,and optimize US-based clinical workflow.This technology has broad prospects for application to hepatic diseases.

Precise cell therapy for liver fibrosis: Endothelial cell and macrophage therapy

Liver fibrosis is typically caused by chronic viral hepatitis and,more recently,fatty liver disease associated with obesity.There are currently no approved drugs for liver cirrhosis,and liver transplantation is limited by donor scarcity,thus driving the investigation of novel therapeutic strategies.The development of liver fibrosis presents with stage-and zone-dependent characteristics that manifest as distinct dynamic changes during vascularization and extracellular matrix(ECM)deposition.However,current cellular therapies do not consider the spatiotem-poral variations of liver fibrosis without identifying the precise location and stage to administer the intervention to achieve optimal therapeutic effects.Herein,we focus on endothelial cell(EC)and macrophage therapy for liver fibrosis because of their important roles in regulating the spatiotemporal changes of vascularization and ECM deposition during liver fibrosis progression.Overall,this review summarizes the stage-dependent EC and macrophage therapy for liver fibrosis,elucidates their respective mechanisms,and exemplifies potential strategies to realize precise cell therapy by targeting specific liver zones.