Objective:To investigate the status of exposure to xylene and Formaldehyde of medical and technical personnel in Pathology Department of a hospital, and to provide references for prevention of occupational hazards.Methods:From July to October in 2019, 52 medical workers and working places in Pathology Department of a third-class hospital in Jiangxi Province were selected as survey objects, the distribution of occupational hazards, protective measures and personal protective equipment were investigated, and the control wind speed of Formaldehyde, xylene and ventilation facilities were detected and analyzed statistically.Results:It showed that the detection rate of xylene and formaldehyde was 82.1% (23/28) , and the detection rate of xylene CSTEL in the two sampling posts was 14.3% (2/14) , the local suction device on each side and the control wind speed of the fume hood do not meet the national standards. Conclusion:It is necessary to strengthen the prevention and control of the occupational hazards in the Department of Pathology to prevent the occurrence of occupational diseases.

Objective:To investigate the status of exposure to xylene and Formaldehyde of medical and technical personnel in Pathology Department of a hospital, and to provide references for prevention of occupational hazards.Methods:From July to October in 2019, 52 medical workers and working places in Pathology Department of a third-class hospital in Jiangxi Province were selected as survey objects, the distribution of occupational hazards, protective measures and personal protective equipment were investigated, and the control wind speed of Formaldehyde, xylene and ventilation facilities were detected and analyzed statistically.Results:It showed that the detection rate of xylene and formaldehyde was 82.1% (23/28) , and the detection rate of xylene CSTEL in the two sampling posts was 14.3% (2/14) , the local suction device on each side and the control wind speed of the fume hood do not meet the national standards. Conclusion:It is necessary to strengthen the prevention and control of the occupational hazards in the Department of Pathology to prevent the occurrence of occupational diseases.

Objective:To evaluate the performance and the accuracy of surgical robot for mandibular reconstruction based on intelligent surgical planning.Methods:115 CT scanning images of normal mandible (57 males, 48 females, 40.3±9.1 years old, from February 2010 to May 2019) and 115 CT scanning images of mandible with tumor (62 males, 53 females, 55.6±7.2 years old, from March 2008 to August 2019) from Shanghai Ninth People’s Hospital were selected. The surgical robot system including work station, UR robot, optical navigation system, 6 dimensional force senor and surgical instrument. A 3D V-Net for mandible preoperational segmentation from CT scans was proposed and used to segment the mandible of a 54-year-old male patient who received mandible reconstruction with fibular flaps. The machine learning algorithm was used to aid surgical planning for maxillo-mandibular defect by detecting landmarks. The accuracy was defined as the distance between corresponding landmarks on the intact mandible. The robot could locate the target according to surgical planning and perform fibula osteotomy through force-motion control. The CT scanning of limb and head from the patient (male, 54 years old) was used for phantom experiments. 30 osteotomies on 5 3D-priented resin phantom were carried out. The pre- and post-operative images were compared to calculate the accuracy. The descriptive results were in the format of Mean±SD.Results:The average accuracy of V-Net for mandible segmentation was 96.581% and the time cost was less than 30 seconds. The average error of feature points on mandible was (2.24±1.74) mm. The residual length error was (1.02±0.45) mm and angle error was (0.96±0.42) degree in robotic-assisted osteotomy according to 3 cases of phantom experiments. The surgical robot could perform osteotomy safely and steadily within 15 min.Conclusions:Intelligent surgical planning can precisely segment the mandible and determine its landmarks. Robot for mandibular reconstruction can perform fibular osteotomy precisely with the pre-operative planning.

Objective:To evaluate the performance and the accuracy of surgical robot for mandibular reconstruction based on intelligent surgical planning.Methods:115 CT scanning images of normal mandible (57 males, 48 females, 40.3±9.1 years old, from February 2010 to May 2019) and 115 CT scanning images of mandible with tumor (62 males, 53 females, 55.6±7.2 years old, from March 2008 to August 2019) from Shanghai Ninth People’s Hospital were selected. The surgical robot system including work station, UR robot, optical navigation system, 6 dimensional force senor and surgical instrument. A 3D V-Net for mandible preoperational segmentation from CT scans was proposed and used to segment the mandible of a 54-year-old male patient who received mandible reconstruction with fibular flaps. The machine learning algorithm was used to aid surgical planning for maxillo-mandibular defect by detecting landmarks. The accuracy was defined as the distance between corresponding landmarks on the intact mandible. The robot could locate the target according to surgical planning and perform fibula osteotomy through force-motion control. The CT scanning of limb and head from the patient (male, 54 years old) was used for phantom experiments. 30 osteotomies on 5 3D-priented resin phantom were carried out. The pre- and post-operative images were compared to calculate the accuracy. The descriptive results were in the format of Mean±SD.Results:The average accuracy of V-Net for mandible segmentation was 96.581% and the time cost was less than 30 seconds. The average error of feature points on mandible was (2.24±1.74) mm. The residual length error was (1.02±0.45) mm and angle error was (0.96±0.42) degree in robotic-assisted osteotomy according to 3 cases of phantom experiments. The surgical robot could perform osteotomy safely and steadily within 15 min.Conclusions:Intelligent surgical planning can precisely segment the mandible and determine its landmarks. Robot for mandibular reconstruction can perform fibular osteotomy precisely with the pre-operative planning.