With the widespread application of robotic technology in surgery, multiple robot-assisted laparoscopic endoscopic surgical systems are currently in the R&D phase, highlighting an urgent need for establishing a scientific and standardized quality evaluation framework. In 2024, the National Medical Products Administration issued the standard YY/T 1941-2024, entitled Robotically-assisted Laparoscopic Surgical System, which specifies performance requirements and test methods for such systems. This standard provides critical guidance for the development of innovative products, technological advancement, and regulatory oversight. The standard offers clear definitions of relevant terminology and systematically standardizes the quality evaluation methods for these products across six key aspects: manual controller performance, instrument arm performance, system operational performance, safety protection functions, robotic surgical instruments, and software. This article interprets the core indicators outlined in the standard, aiming to assist clinicians in gaining a deeper understanding of the performance parameters of these systems and to support manufacturers in conducting quality assessments and risk analyses during product development, thereby promoting high-quality progress in the industry and related products.

With the deepening application of artificial intelligence (AI) technology in the field of medical devices, pre-trained models have increasingly become a crucial engine driving innovation in intelligent healthcare due to their efficiency, generalization capability, and transfer learning performance. However, potential risks associated with pre-trained models—such as issues related to source diversity and quality controllability —pose new challenges to the safety and effectiveness of AI-based medical devices. Against this background, the National Medical Products Administration (NMPA) released the sectoral standard YY/T 1833.5-2024 Artificial Intelligence Medical Devices—Quality Requirements and Evaluation-Part 5: Pre-trained Models in September 2024. This standard provides essential technical guidance and a regulatory framework for standardizing the application of pre-trained models in medical devices, marking a milestone in ensuring the safety and efficacy of AI-powered medical products. This article offers an in-depth interpretation and analysis of the standard's background, orientation, and key technical consideration. It elucidates specific requirements regarding documentation for pre-trained models, definitions of critical quality attributes, and conformity assessment pathways. Furthermore, the practical and far-reaching implications of the standard are discussed, including its role in enhancing quality assurance throughout the entire lifecycle of AI-based medical devices and guiding technological innovation and healthy development within the industry. Additionally, by dissecting the standard, this article aims to support the industry in conducting prudent evaluations during model selection phases, thereby reducing the application of low-quality and high-risk models.

OBJECTIVES: Digitally collect data on the preparation space of the molar occlusal surface for full crown restoration and analyze the minimum preparation spaces and areas prone to insufficient preparation. METHODS: A total of 846 molars (excluding third molars) designated for repair with zirconium dioxide crowns were selected. All molars were prepared by using the fixed-depth groove method under visual guidance. A digital impression was collected, and the occlusal surface of the preparation was divided into regions: mesiobuccal area, mesiolingual area, distobuccal area, distolingual area, mesial marginal ridge, and distal marginal ridge. Image measurement software (3Shape Unite) was employed to record the smallest preparation space on the molar occlusal surface, and a space of <1 mm was defined as insufficient preparation. The chi-square test was utilized for the statistical analysis of data. RESULTS: Significant differences were observed in the areas with the smallest preparation space on the occlusal surface of molars across different quadrants (P<0.05). Notably, the area with the smallest preparation space on the occlusal surface of the maxillary first molar was most frequently found in the mesiobuccal area, whereas the smallest preparation spaces on other molars were predominantly located in the distolingual area. The insufficient preparation of the occlusal surface occurred in 41.0% (347 cases) of teeth. The distribution of prone areas corresponded with the distribution of the smallest spatial areas. CONCLUSIONS Although the fixed-depth groove method is employed, the preparation space on the molar surface unavoidably remains uneven under visual guidance. The identification of the areas prone to minimum preparation spaces can serve as a valuable guide for clinicians to prevent insufficient preparation.
Humans ; Molar/anatomy & histology* ; Crowns ; Zirconium

In recent years,robot-assisted technology has become increasingly sophisticated in the field of orthopedic surgical navigation.To ensure surgical safety and protect patient rights,corresponding products must undergo rigorous and standardized pre-market quality evaluations.In 2023,the National Medical Products Administration issued the sectoral standard YY/T 1901-2023 Requirements and Test Methods for Orthopaedic Surgical Navigation Equipment Employing Robotic Technology.This standard specifies the performance require-ments for robotic orthopedic surgical navigation systems across ten key aspects:general requirements,robotic arm positioning device performance,tracking device performance,system performance,installation repeatability,protective functions,orthopedic powered surgical instruments,liquid cooling,positioning acces-sory requirements,and software requirements.Additionally,it standardizes quality evaluation methods,provi-ding significant guidance for advancing robotic technology,accelerating product market entry,and strengthe-ning regulatory oversight.This article analyzes the key components of the standard to facilitate better quality assessment of products and promote high-quality development within the industry.

In recent years,with the rapid development of the artificial intelligence(AI)medical device industry,regulatory authorities have been continuously strengthening quality control over datasets.In 2024,the National Medical Products Administration(NMPA)issued the significant industry standard YY/T 1949-2024 Artificial Intelligence Medical Devices—Particular Requirements for Datasets:Color Fundus Photographs of Dia-betic Retinopathy,which is China's first specialized standard for evaluating dataset quality in AI medical devices targeting a specific disease area.Under the existing general standards framework for AI medical devices,this standard focuses specifically on diabetic retinopathy fundus photograph datasets,systematically proposing spe-cialized quality requirements and testing methods.Its implementation will provide clear technical guidance for dataset manufacturers,help improve the development quality of AI-assisted diagnostic software for diabetic retinopathy,and promote standardized industry advancement.

In recent years,robot-assisted technology has become increasingly sophisticated in the field of orthopedic surgical navigation.To ensure surgical safety and protect patient rights,corresponding products must undergo rigorous and standardized pre-market quality evaluations.In 2023,the National Medical Products Administration issued the sectoral standard YY/T 1901-2023 Requirements and Test Methods for Orthopaedic Surgical Navigation Equipment Employing Robotic Technology.This standard specifies the performance require-ments for robotic orthopedic surgical navigation systems across ten key aspects:general requirements,robotic arm positioning device performance,tracking device performance,system performance,installation repeatability,protective functions,orthopedic powered surgical instruments,liquid cooling,positioning acces-sory requirements,and software requirements.Additionally,it standardizes quality evaluation methods,provi-ding significant guidance for advancing robotic technology,accelerating product market entry,and strengthe-ning regulatory oversight.This article analyzes the key components of the standard to facilitate better quality assessment of products and promote high-quality development within the industry.

In recent years,with the rapid development of the artificial intelligence(AI)medical device industry,regulatory authorities have been continuously strengthening quality control over datasets.In 2024,the National Medical Products Administration(NMPA)issued the significant industry standard YY/T 1949-2024 Artificial Intelligence Medical Devices—Particular Requirements for Datasets:Color Fundus Photographs of Dia-betic Retinopathy,which is China's first specialized standard for evaluating dataset quality in AI medical devices targeting a specific disease area.Under the existing general standards framework for AI medical devices,this standard focuses specifically on diabetic retinopathy fundus photograph datasets,systematically proposing spe-cialized quality requirements and testing methods.Its implementation will provide clear technical guidance for dataset manufacturers,help improve the development quality of AI-assisted diagnostic software for diabetic retinopathy,and promote standardized industry advancement.

Objective In this study,we compared the masticatory efficiency,occlusal functional indexes,and satisfaction of lingual-ized occlusion(LO)vs.anatomic occlusion(AO)and discussed the difference between these occlusions.Methods Twenty edentulous patients were included in this clinical trial during 2021 April to December.They were divided into 2 groups according to their denture occlusion.Masticatory efficiency was tested by chewing the same amount of peanuts(by weight)and then the absorption of the solution was compared under a spectrophotometer after wearing dentures for 1 month.The TeeTester occlusion analysis system was used to test masticatory function,and satisfaction research was conducted for the edentulous patients at this time point as well.Finally,statistical analyses were conducted to compare the difference in masticatory efficiency between AO and LO by SPSS 20.0.Results The indexes of LO were worse than AO in masticatory efficiency,masticatory function and most of satisfaction research,while LO was better than AO in speech recovery.The difference was only statistically significant in COF-y of masticatory function(P＜0.05).Conclusion LO is similar with AO in terms of masticatory efficiency,satisfaction and denture stability;LO has good performance in denture's fit and use for patients.

ObjectiveTo explore the effect of mentality-controlling on local microcirculation of acupuncture at Hegu （LI 4）. MethodsForty healthy subjects were randomly divided into simple acupuncture group and mentality-controlling acupuncture group， with 20 cases in each group. The simple acupuncture group was acupunctured at Hegu （LI 4） on the right hand of the patient. In mentality-controlling acupuncture group， an audio clip was played to guide the participants to control their mentality when needle retention for 5 min after performing acupuncture twirling mani-pulation. The microcirculatory blood perfusion units （MBPU） was captured and compared between groups of participants using laser speckle contrast imaging （LSCI） for 5 mins in each of the four stages， state before acupuncture （R1）， after needle insertion （A1）， after needle twirling （A2） and after needle withdrawal （R2）. The participants' feelings such as relaxation， concentration， drowsiness， energy flow at acupoints were compared between groups， and the adverse reactions were recorded. ResultsThe MBPU values of R1， A1， A2 and R2 stages in mentality-controlling acupuncture group were 67.92±18.40 PU， 168.13±46.03 PU， 144.19±44.40 PU， 159.73±65.21 PU， respectively. The simple acupuncture group were 79.85 （66.80， 99.40）PU， 193.31 （173.15， 224.80）PU， 147.68 （128.12， 198.30）PU， 158.09 （126.05， 207.59）PU respectively. There was a statistically significant difference in MBPU between groups of the four stages （P＜0.01）. Among them， the MBPU in the A1 stage of both groups was significantly larger than that in the R1 stage， the MBPU in the A2 stage significantly smaller than that in the A1 stage， and the MBPU in the R2 stage was significantly larger than that in the R1 stage （P＜0.01）. MBPU in the R2 stage of the mentality-controlling acupuncture group was larger than that in the A2 stage （P＜0.05）. The difference of MBPU between A2 and A1 in mentality-controlling acupuncture group was significantly smaller than that in the simple acupuncture group， and the difference was statistically significant （t = 3.79， P＜0.01）. In mentality-controlling acupuncture group， 18 participants felt relaxed （90%）， 15 concentrated （75%）， 3 felt sleepy （15%）， and 14 felt energy flow at acupoints （70%）； while in the simple acupuncture group， 13 participants felt relaxed and sleepy （65%）， 6 concentrated and 6 felt energy flow at acupoints （30%）. The number of participants with concentration and energy flow at acupoints in mentality-controlling acupuncture group was higher than that in the simple acupuncture group， and the number felt sleepy was lower than that in the simple acupuncture group （P＜0.05）. No needle fainting， needle site infection， local haematoma and any other adverse reactions were found in both groups. ConclusionAcupuncture could affect the changes of local microcirculation， and controlling mentality during acupuncture can slow the rate of MBPU reduction and enhance the post-acupuncture effect with a favourable safety profile.

Objective This study aimed to evaluate the application of digital impression and resin model technology in removable partial dentures(RPD)for Kennedy classⅠandⅡdentition defects.Methods Patients with Kennedy classⅠorⅡdental defect were selected and grouped in accordance with the following denture production processes:digital impression/resin model/cast cobalt-chromium alloy framework group(group A),digital impression/resin model/laser printed titanium framework group(group B),alginate impression/plaster model/cast cobalt-chromium alloy framework group(group C),and alginate impression/plaster model/laser printed titanium framework group(group D),with 40 cases in each group.The final RPD was examined in place in the mouth,and the evaluation indicators included the retention force of clamp ring,the tightness of connector and base,and the accuracy of occlusion.The evaluation scores of each in-dex were used for analysis on the Kruskal-Wallis rank-sum test.Results No statistically significant difference in the score of each index was found among the four groups in RPD.Conclusion The cast cobalt-chromium alloy and laser-printed titanium framework RPD using digital impression and resin model can meet the clinical restoration requirements of patients with Kennedy classⅠandⅡdentition defects.