Background and Objectives: Lung cancer is the commonest cancer killer in the western world. Many patients have lung cancers first identified on chest radiograph (CXR). Potentially curable cancers are often missed on CXR. This study quantified the incidence of cases of lung cancer which were initially overlooked and studied the causes of delayed diagnosis. Materials and Methods: All consecutive patients discussed during a 3-month period at the local lung cancer multidisciplinary meeting (MDM) were identified. All imaging within two years prior to diagnosis of lung cancer were reviewed with its report. Any CXR examination which failed to raise the potential for lung cancer was blindly reviewed by four consultant chest radiologists. Results: 189 patients were identified from the MDM over three months. 38,049 CXRs were carried out in the trust over the same period. Of the 189 patients, 58 had previous CXRs within 2 years reported as normal. On review 27 (47%) showed an abnormality in the region of the lung subsequently shown to have cancer. 70% of lesions were central, obscured by the heart, diaphragm, clavicles or mediastinum. Conclusions: This study shows that 1 in 1,409 CXRs reported as normal harbours a visible lung cancer on retrospective review. In this group 14% of patients with lung cancer could potentially have been diagnosed earlier. Of those that had previous CXRs, 47% had abnormal CXRs reported as normal. This study qualifies the rate of missed lung cancer on CXR in clinical practice and highlights where on CXR cancers are missed.

Background and Objectives: Pediatric femoral neck fracture is a serious and disabling injury that is prone to complications. This study examined the feasibility, accuracy, and efficiency of treating the fracture through a three-dimensional (3D)-printed navigation template that was used to guide placement of cannulated screws and locking compression-pediatric hip plates (LC-PHPs). Materials and Methods: Template-guided surgeries were carried out on nine children with femoral neck fracture from 1, 2012 to 12, 2014, and the resulting data were analyzed retrospectively. From preoperative computed tomography data, a 3D model of the proximal femur and a matching navigation template were created for guiding placement of cannulated screws or LC-PHP. Finally, the template-guided operation was performed, and the outcomes were compared to those from control patients undergoing the same surgery without navigation templates (n = 10). Results: The navigation templates were found to match the individual proximal femurs well. Two to three screws were accurately inserted in the femoral neck, and the end of the fracture was successfully stabilized. Implantation of the cannulated screws or LC-PHP took an average of 13.6 and 24.5 min, respectively, whereas intraoperative X-ray was used an average of 4.2 times for the former and 5.5 times for the latter. This was compared to 37.6 and 59.6 min, and 11.4 and 15.4 X-rays, for the controls. Postoperative X-ray showed a great reduction of the femoral neck fracture. Six- to twelve-month follow-ups indicated that the fracture had healed and the function of hip joint was excellent for seven of the children and good for two (Ratliff's criteria). Conclusions: Using 3D-printed guides, accurate and effective placement of cannulated screws and LC-PHPs is realized in the femoral neck. The method reduces operation time, intraoperative bleeding, radiation exposure, and iatrogenic damage to the vasculature, femoral neck, and epiphysis.

Fuzzy nature of decision-making process in healthcare enforces technology producers and researchers to employ creative and smooth solutions. Conversion from fuzzy concepts and ideas to crisp values causes loss of precision and weakens the output decisions. A promising bundle of techniques, soft computing, is a fast developing and popular area that helps meet this creative and smooth need in healthcare. In this study, fuzzy logic (FL) application in healthcare decision-making is examined. The number of publications is rising each year related to FL application in healthcare. FL can be used as a classifier, or in a selection process of a certain type of disease, or diseased patients, or determining the risk ratio of a disease, or in a data mining algorithm, or in constructing a decision support system. This study is a descriptive study aiming to examine and explain FL applications in healthcare.