Negative Pressure Wound Therapy for Gas Gangrene of the Fingertip with Prolonged Infection.

Background: We encountered a case of infected soft tissue defect of the fingertip treated using negative pressure wound therapy (NPWT). The development of NPWT was started in the early 1990s, and it is a relatively new treatment method included in insurance coverage in Japan in 2010. NPWT is used for intractable wounds; some reports have examined its use on infected wounds. However, to the best of our knowledge, no study has examined its use on infected fingertip wounds. Methods: A patient with an infected soft tissue defect in the fingertip whose epithelialization period was prolonged despite continued antibiotic therapy was treated using NPWT in combination. Results: After NPWT was started, signs of infection and wound granulation were good. Additionally, completion of epithelialization was confirmed 7 weeks after NPWT started. Conclusions: Conventionally, skin flap or graft by hand surgeons have been performed on fingertip soft tissue defects with infection. NPWT does not require specialized and advanced surgical techniques; treatment for infected soft tissue defects can be administered by anyone if they have the required skills. In conclusion, NPWT may be considered a suitable alternative when treatment options such as flaps and skin grafts are not feasible.

Detection of Sacral Fractures on Radiographs Using Artificial Intelligence.

Sacral fractures are often difficult to diagnose on radiographs. Computed tomography (CT) and magnetic resonance imaging (MRI) can improve the detection rate but cannot always be performed. The accuracy of artificial intelligence (AI) in detecting orthopaedic fractures is now comparable with that of orthopaedic specialists. However, the ability of AI to detect sacral fractures has not been investigated, to our knowledge. We hypothesized that the ability to detect sacral fractures on radiographs could be improved by using AI, and aimed to develop an AI model to detect sacral fractures accurately on radiographs with better accuracy than that of orthopaedic surgeons. Methods: Subjects were patients with suspected pelvic fractures for whom radiographs and CT scans had been obtained. The radiographs were labeled according to sacral fracture status based on CT results. The data set was divided into a training set (2,038 images) and a test set (200 images). Eight convolutional neural network (CNN) models were trained using the training set. Post-trained models were used to evaluate their discrimination ability. The detection ability of 4 experienced orthopaedic surgeons was also measured using the same test set. The results of fracture assessment by the orthopaedic surgeons were compared with those of the 3 CNNs with the greatest area under the receiver operating characteristic curve. Results: Among the 8 trained models, the highest areas under the curve were for InceptionV3 (0.989), Xception (0.987), and Inception ResNetV2 (0.984). The detection rate was significantly higher for these 3 CNNs than for the orthopaedic surgeons. Conclusions: By enhancing the processing of probabilistic tasks and the communication of their results, AI may be better able to detect sacral fractures than orthopaedic surgeons. Level of Evidence: Diagnostic Level III. See Instructions for Authors for a complete description of levels of evidence.