Automatic segmentation of inconstant fractured fragments for tibia/fibula from CT images using deep learning.

Orthopaedic surgeons need to correctly identify bone fragments using 2D/3D CT images before trauma surgery. Advances in deep learning technology provide good insights into trauma surgery over manual diagnosis. This study demonstrates the application of the DeepLab v3+ -based deep learning model for the automatic segmentation of fragments of the fractured tibia and fibula from CT images and the results of the evaluation of the performance of the automatic segmentation. The deep learning model, which was trained using over 11 million images, showed good performance with a global accuracy of 98.92%, a weighted intersection over the union of 0.9841, and a mean boundary F1 score of 0.8921. Moreover, deep learning performed 5-8 times faster than the experts' recognition performed manually, which is comparatively inefficient, with almost the same significance. This study will play an important role in preoperative surgical planning for trauma surgery with convenience and speed.

Deep Learning Model Based on You Only Look Once Algorithm for Detection and Visualization of Fracture Areas in Three-Dimensional Skeletal Images.

Utilizing "You only look once" (YOLO) v4 AI offers valuable support in fracture detection and diagnostic decision-making. The purpose of this study was to help doctors to detect and diagnose fractures more accurately and intuitively, with fewer errors. The data accepted into the backbone are diversified through CSPDarkNet-53. Feature maps are extracted using Spatial Pyramid Pooling and a Path Aggregation Network in the neck part. The head part aggregates and generates the final output. All bounding boxes by the YOLO v4 are mapped onto the 3D reconstructed bone images after being resized to match the same region as shown in the 2D CT images. The YOLO v4-based AI model was evaluated through precision-recall (PR) curves and the intersection over union (IoU). Our proposed system facilitated an intuitive display of the fractured area through a distinctive red mask overlaid on the 3D reconstructed bone images. The high average precision values (>0.60) were reported as 0.71 and 0.81 from the PR curves of the tibia and elbow, respectively. The IoU values were calculated as 0.6327 (tibia) and 0.6638 (elbow). When utilized by orthopedic surgeons in real clinical scenarios, this AI-powered 3D diagnosis support system could enable a quick and accurate trauma diagnosis.

Occurrence and genotypic characteristics of Enterocytozoon bieneusi in pigs with diarrhea.

There is limited information available on the association between Enterocytozoon bieneusi and diseases in animals or on the characteristics of the strains involved. This study examined the occurrence of E. bieneusi in piglets with and without diarrhea to determine its involvement. Among 472 fecal samples from 472 piglets (237 with diarrhea and 235 without) up to 7 weeks of age, 67 (approximately 14%) were polymerase chain reaction (PCR) positive for E. bieneusi. Of the 237 piglets with diarrhea, 38 (approximately 16%) tested positive for E. bieneusi. Of the 235 healthy piglets, 29 (approximately 12%) tested positive for E. bieneusi. This species was detected only in the younger group of piglets with diarrhea, particularly those aged less than 1 week and between 1 and 2 weeks. This suggests that E. bieneusi is a possible cause of diarrhea in piglets. This organism, however, produced asymptomatic infections in the older piglets, as there was no significant difference in the rates of occurrence between the diarrheic and nondiarrheic older piglets (aged older than 4 weeks). The internal transcribed spacer (ITS) region of the ribosomal ribonucleic acid gene of the ten E. bieneusi-positive samples was amplified using nested PCR and subsequently sequenced. Genetic polymorphisms, which were represented by five distinct genotypes (PEbA-PEbE), were found among the E. bieneusi isolates. The five genotypes identified in this study differed from each other by two to six single-nucleotide polymorphisms. Nine isolates from four genotypes (PEbA-PEbD) were homologous to previously known types that had originally been isolated from pigs. However, one isolate from the PEbE genotype was identical to type CAF1, which was originally isolated from humans. In addition, the phylogenetic relationships determined by the neighbor-joining analysis of the ITS sequences indicated this genotype to be more distant from the other pig-specific genotypes. Thus, this isolate from pigs may be distantly related to the pig-specific genotypes and may be capable of infecting humans.

Antimicrobial resistance of Escherichia coli O157 from cattle in Korea.

Of 45 Escherichia coli O157 isolates from cattle feces, which were collected between May 2000 and September 2003 in Korea, 32 were resistant to at least 1 antibiotic and 28 were resistant to 4 or more antibiotics, with 32, 30 and 30 of the isolates being resistant to streptomycin, tetracycline and sulfisoxazole, respectively. Two isolates were resistant to fluoroquinolones and to 10 or more of the 22 other antimicrobial agents that were tested. Thirteen antimicrobial resistant patterns were observed. The most frequent resistance type, which was found for 11 isolates, was streptomycin-tetracycline-kanamycin-ampicillin-piperacillin-cephalothin-sulfisoxazole-ticarcillin. Polymerase chain reaction (PCR) analysis of the isolates for E. coli O157 virulence markers revealed that 25 of the resistant E. coli O157 isolates tested positive for stx2 or both stx1 and stx2 genes. These findings suggest that many of the resistant E. coli O157 isolates might cause disease in humans.