Case report: Quantitative recognition of virtual human technology acceptance based on efficient deep neural network algorithm.

With the advancement of artificial intelligence, robotics education has been a significant way to enhance students' digital competency. In turn, the willingness of teachers to embrace robotics education is related to the effectiveness of robotics education implementation and the sustainability of robotics education. Two hundred and sixty-nine teachers who participated in the "virtual human education in primary and secondary schools in Guangdong and Henan" and the questionnaire were used as the subjects of study. UTAUT model and its corresponding scale were modified by deep learning algorithms to investigate and analyze teachers' acceptance of robotics education in four dimensions: performance expectations, effort expectations, community influence and enabling conditions. Findings show that 53.68% of the teachers were progressively exposed to robotics education in the last three years, which is related to the context of the rise of robotics education in schooling in recent years, where contributing conditions have a direct and significant impact on teachers' acceptance of robotics education. The correlation coefficients between teacher performance expectations, effort expectations, community influence, and enabling conditions and acceptance were 0.290 (p = 0.000<0.001), -0.144 (p = 0.048<0.05), 0.396 (p = 0.000<0.001), and 0.422 (p = 0.000<0.001) respectively, indicating that these four core dimensions both had a significant effect on acceptance. Optimization comparison results of deep learning models show that mDAE and AmDAE provide a substantial reduction in training time compared to existing noise-reducing autoencoder models. It is shown that time-complexity of the deep neural network algorithm is positively related to the number of layers of the model.

[From digital virtual human, three-dimensional visualization to digital intelligence in liver surgery].

Digital intelligent hepatobiliary surgery has evolved over decades.It has experienced an evolution course from digital virtual human technology to the establishment of a quality-controlled and homogeneous three-dimensional visualization system for precision diagnosis and treatment of diseases, from three-dimensional visualization to the clinical transformation of digital intelligent technology and changes in the diagnosis and treatment model, from empirical diagnosis of diseases to the application of deep learning for the intelligent diagnosis and treatment of diseases, from empirical surgery to real-time multi-modal image guidance during surgery, and from the morphological diagnosis of tumors to accurate diagnosis from molecular imaging.During the whole process, only through continuous innovation in research, theory and technology can the "life" of digital intelligent surgery be endowed with new vitality.In the future, the definition of tumor boundary from the molecular and cellular levels and the early diagnosis and treatment of liver tumor through the functional visualization of key molecules will have significant clinical value for changing the prognosis of liver cancer.In addition, in order to realize intelligent navigation for hepatectomy and break through the technical bottleneck, it is of great clinical significance to develop an intelligent robot real-time navigation hepatectomy system with automatic navigation technology, machine learning intelligent planning technology and multimodal image fusion technology.This provides unprecedented opportunities and challenges for the development of digital intelligent hepatobiliary surgery.

From digital virtual human, three-dimensional visualization to digital intelligence in liver surgery / 中华外科杂志

Digital intelligent hepatobiliary surgery has evolved over decades.It has experienced an evolution course from digital virtual human technology to the establishment of a quality-controlled and homogeneous three-dimensional visualization system for precision diagnosis and treatment of diseases, from three-dimensional visualization to the clinical transformation of digital intelligent technology and changes in the diagnosis and treatment model, from empirical diagnosis of diseases to the application of deep learning for the intelligent diagnosis and treatment of diseases, from empirical surgery to real-time multi-modal image guidance during surgery, and from the morphological diagnosis of tumors to accurate diagnosis from molecular imaging.During the whole process, only through continuous innovation in research, theory and technology can the "life" of digital intelligent surgery be endowed with new vitality.In the future, the definition of tumor boundary from the molecular and cellular levels and the early diagnosis and treatment of liver tumor through the functional visualization of key molecules will have significant clinical value for changing the prognosis of liver cancer.In addition, in order to realize intelligent navigation for hepatectomy and break through the technical bottleneck, it is of great clinical significance to develop an intelligent robot real-time navigation hepatectomy system with automatic navigation technology, machine learning intelligent planning technology and multimodal image fusion technology.This provides unprecedented opportunities and challenges for the development of digital intelligent hepatobiliary surgery.