Naomi Yagi

Pelvic fractures pose significant challenges in medical diagnosis due to the complex structure of the pelvic bones. Timely diagnosis of pelvic fractures is critical to reduce complications and mortality rates. While computed tomography (CT) is highly accurate in detecting pelvic fractures, the initial diagnostic procedure usually involves pelvic X-rays (PXR). In recent years, many deep learning-based methods have been developed utilizing ImageNet-based transfer learning for diagnosing hip and pelvic fractures. However, the ImageNet dataset contains natural RGB images which are different than PXR. In this study, we proposed a two-step transfer learning approach that improved the diagnosis of pelvic fractures in PXR images. The first step involved training a deep convolutional neural network (DCNN) using synthesized PXR images derived from 3D-CT by digitally reconstructed radiographs (DRR). In the second step, the classification layers of the DCNN were fine-tuned using acquired PXR images. The performance of the proposed method was compared with the conventional ImageNet-based transfer learning method. Experimental results demonstrated that the proposed DRR-based method, using 20 synthesized PXR images for each CT, achieved superior performance with the area under the receiver operating characteristic curves (AUROCs) of 0.9327 and 0.8014 for visible and invisible fractures, respectively. The ImageNet-based method yields AUROCs of 0.8908 and 0.7308 for visible and invisible fractures, respectively.

PURPOSE: This study examined the impact of frailty and prefrailty on mid-term outcomes and rehabilitation courses after cardiac surgery. METHODS: A total of 261 patients (median age: 73 years; 30% female) who underwent elective cardiac surgery were enrolled in this study. The Japanese version of the Cardiovascular Health Study Frailty Index classified 86, 131, and 44 patients into frailty, prefrailty, and robust groups, respectively. We examined the recovery of walking ability, outcomes at discharge, mid-term all-cause mortality, and rehospitalization related to major adverse cardiovascular and cerebrovascular events (MACCE) across the three cohorts. RESULTS: The 3-year survival rates in the frailty, prefrailty, and robust groups were 87%, 97%, and 100%, respectively (p = 0.003). The free event rates of all-cause mortality and re-hospitalization related to MACCE were 59%, 79%, and 95%, respectively (p < 0.001), with a graded elevation in adjusted morbidity among patients in the prefrailty (hazard ratio [HR], 4.57; 95% confidence interval [CI], 1.08-19.4) and frailty (HR, 9.29; 95% CI 2.21-39.1) groups. Patients with frailty also experienced a delayed recovery of walking ability and a reduced number of patients with frailty were discharged home. CONCLUSION: Frailty and prefrailty adversely affect the mid-term prognosis and rehabilitation course after cardiac surgery.