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Purpose In our hospital, cases of bone and soft tissue infections have been treated with continuous local antibiotics perfusion that allows for continuous circulation of antibiotics throughout the infected lesion. We termed this treatment “intramedullary antibiotics perfusion (iMAP)” for bone infection such as fracture-related infection (FRI) and “intrasoft tissue antibiotics perfusion” for soft tissue infection. Many cases are treated with both modalities. To introduce iMAP, this study focused on the patients with FRI treated with iMAP and reviewed their treatment outcomes. Methods We included 10 patients with FRI treated with iMAP between 2004 and 2017. The iMAP needles were inserted near the infected lesion, and an aminoglycoside antimicrobial was continuously administered. Patient characteristics, pathogenic bacteria, administered antibiotics, duration of administration, concentrations of antibiotics in blood and leachate fluid, fracture union rate, implant retention rate, and complications were studied. Results The mean age of patients was 59.9 years, and the mean follow-up period was 2.5 years. Affected bones were the tibia ( n = 8), humerus ( n = 1), and fibula ( n = 1). Deep infections developed on average 29.9 days after osteosynthesis. Pathogenic bacteria were methicillin-susceptible Staphylococcus aureus ( n = 6), methicillin-resistant S. aureus ( n = 2), and unknown ( n = 2). Average iMAP duration was 17.1 days. In all patients, infection was eradicated while preserving the implants, and fracture union was achieved without complications. Conclusion iMAP is a novel local drug delivery system allowing high concentrations of antibiotics to be administered without complications and is useful in the treatment of FRI.

III.

Pelvic fracture is one of the leading causes of death in the elderly, carrying a high risk of death within 1 year of fracture. This study proposes an automated method to detect pelvic fractures on 3-dimensional computed tomography (3D-CT). Deep convolutional neural networks (DCNNs) have been used for lesion detection on 2D and 3D medical images. However, training a DCNN directly using 3D images is complicated, computationally costly, and requires large amounts of training data. We propose a method that evaluates multiple, 2D, real-time object detection systems (YOLOv3 models) in parallel, in which each YOLOv3 model is trained using differently orientated 2D slab images reconstructed from 3D-CT. We assume that an appropriate reconstruction orientation would exist to optimally characterize image features of bone fractures on 3D-CT. Multiple YOLOv3 models in parallel detect 2D fracture candidates in different orientations simultaneously. The 3D fracture region is then obtained by integrating the 2D fracture candidates. The proposed method was validated in 93 subjects with bone fractures. Area under the curve (AUC) was 0.824, with 0.805 recall and 0.907 precision. The AUC with a single orientation was 0.652. This method was then applied to 112 subjects without bone fractures to evaluate over-detection. The proposed method successfully detected no bone fractures in all except 4 non-fracture subjects (96.4%).

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