PURPOSE: Intertrochanteric fracture (ITF) classification is crucial for surgical decision-making. However, orthopedic trauma surgeons have shown lower accuracy in ITF classification than expected. The objective of this study was to utilize an artificial intelligence (AI) method to improve the accuracy of ITF classification. METHODS: We trained a network called YOLOX-SwinT, which is based on the You Only Look Once X (YOLOX) object detection network with Swin Transformer (SwinT) as the backbone architecture, using 762 radiographic ITF examinations as the training set. Subsequently, we recruited 5 senior orthopedic trauma surgeons (SOTS) and 5 junior orthopedic trauma surgeons (JOTS) to classify the 85 original images in the test set, as well as the images with the prediction results of the network model in sequence. Statistical analysis was performed using the SPSS 20.0 (IBM Corp., Armonk, NY, USA) to compare the differences among the SOTS, JOTS, SOTS + AI, JOTS + AI, SOTS + JOTS, and SOTS + JOTS + AI groups. All images were classified according to the AO/OTA 2018 classification system by 2 experienced trauma surgeons and verified by another expert in this field. Based on the actual clinical needs, after discussion, we integrated 8 subgroups into 5 new subgroups, and the dataset was divided into training, validation, and test sets by the ratio of 8:1:1. RESULTS: The mean average precision at the intersection over union (IoU) of 0.5 (mAP50) for subgroup detection reached 90.29%. The classification accuracy values of SOTS, JOTS, SOTS + AI, and JOTS + AI groups were 56.24% ± 4.02%, 35.29% ± 18.07%, 79.53% ± 7.14%, and 71.53% ± 5.22%, respectively. The paired t-test results showed that the difference between the SOTS and SOTS + AI groups was statistically significant, as well as the difference between the JOTS and JOTS + AI groups, and the SOTS + JOTS and SOTS + JOTS + AI groups. Moreover, the difference between the SOTS + JOTS and SOTS + JOTS + AI groups in each subgroup was statistically significant, with all p < 0.05. The independent samples t-test results showed that the difference between the SOTS and JOTS groups was statistically significant, while the difference between the SOTS + AI and JOTS + AI groups was not statistically significant. With the assistance of AI, the subgroup classification accuracy of both SOTS and JOTS was significantly improved, and JOTS achieved the same level as SOTS. CONCLUSION In conclusion, the YOLOX-SwinT network algorithm enhances the accuracy of AO/OTA subgroups classification of ITF by orthopedic trauma surgeons.
Humans ; Hip Fractures/diagnostic imaging* ; Orthopedic Surgeons ; Algorithms ; Artificial Intelligence

OBJECTIVEThis study aims to investigate the bacteria contamination on hands of funeral staffs in different positions.

METHODSBacterial samples were collected from the hands of 105 funeral staffs in different positions (including 90 frontline staffs and 15 administrative workers) from 13 funeral parlors nationwide, and were subsequently tested by bacterium inspection.

RESULTSIn total, 1783 strains of bacteria were isolated, including 1027 Gram-positive bacteria, most of which were Staphylococcus; and 756 Gram-negative bacteria, most of which were Pseudomonas. Out of the 1783 strains of bacteria, 570 pathogens and opportunistic pathogens were isolated, accounted to 31.96%. The isolated ratio of pathogens and conditional pathogens in embalmed/cosmetologist of cadavers was 35.67% (370/1037), which was higher than those in the funeral workers in other positions, such as cremators, pick-up and administrative workers, whose ratios were 24.42% (95/389), 22.41% (52/232) and 10.40% (12/125), respectively (χ(2) were 13.682, 10.967 and 32.263, respectively; P values were all < 0.05). And the isolated ratios of pathogens and conditional pathogens in cremators and pick-up workers were significantly higher than that in administrative workers (χ(2) were 11.206 and 7.873, respectively; P values were all < 0.05).

CONCLUSIONLots of bacteria were found in the samples from hands of funeral staffs. The isolated ratio of pathogens and conditional pathogens was different between the funeral staffs in different positions; while the highest was from embalmed/cosmetologist of cadavers and the lowest was from administrators.

Bacteria ; isolation & purification ; Hand ; microbiology ; Humans ; Microbial Sensitivity Tests ; Mortuary Practice ; Occupational Exposure