No abstract

THORACIC IMAGINGC hest radiography is the most common radiologic examination, despite its inferiority to low-dose CT, for lung cancer screening (1). Some authors showed that up to 90% of "missed" lung cancer nodules can be found when the baseline chest radiograph is re-reviewed with the benefit of the follow-up examination showing the mass that has grown in size (2). Misdiagnoses of lung cancer can occur for many reasons. This oversight can be due to a lack of perception of the nodule, the decision to ignore a subtle density, and the satisfaction of search when another abnormality is identified (3-5). Lesion characteristics including size, density, and location make the detection of lung nodules more challenging on chest radiographs (6-8).To improve the efficacy of chest radiography for nodule detection, computer-aided detection (CAD) software has been developed and evaluated. In 2004, Kakeda et al (9) tested their CAD and reported that it was beneficial in analyzing radiographs with nodules but had an average falsepositive rate of 3.15 per image. de Hoop et al (10) showed Purpose: To compare the performance of radiologists in detecting malignant pulmonary nodules on chest radiographs when assisted by deep learning-based DCNN software with that of radiologists or DCNN software alone in a multicenter setting. Materials and Methods: Investigators at four medical centers retrospectively identified 600 lung cancer-containing chest radiographs and 200 normal chest radiographs. Each radiograph with a lung cancer had at least one malignant nodule confirmed by CT and pathologic examination. Twelve radiologists from the four centers independently analyzed the chest radiographs and marked regions of interest. Commercially available deep learning-based computer-aided detection software separately trained, tested, and validated with 19 330 radiographs was used to find suspicious nodules. The radiologists then reviewed the images with the assistance of DCNN software. The sensitivity and number of false-positive findings per image of DCNN software, radiologists alone, and radiologists with the use of DCNN software were analyzed by using logistic regression and Poisson regression.

AI improves radiologist's bone age assessment by increasing accuracy and decreasing variability and RMSE. The utilization of AI by radiologists improves performance compared to AI alone, a radiologist alone, or a pooled cohort of experts. This suggests that AI may optimally be utilized as an adjunct to radiologist interpretation of imaging studies to improve performance.

PurposeToxocariasis is the most common cause of peripheral blood eosinophilia in Korea and produces eosinophilic infiltration in various organs, including the lung. However, the prevalence of toxocariasis in the general population is rarely reported.MethodsWe investigated the seroprevalence of Toxocara larval antibody among asymptomatic people who attended Samsung Medical Center for a health checkup, including low-dose chest computed tomography (CT) between March 2012 and December 2013. A total of 633 people (400 men and 233 women) were prospectively recruited.ResultsThe Toxocara-seropositive rate was 51.2% using the current cutoff value based on Toxocara enzyme-linked immunosorbent assay (ELISA) (67.0% for men and 24.0% for women). In the multivariate-adjusted model, age (odds ratio [OR], 1.08; 95% confidence intervals [CI], 1.04-1.11), male sex (OR, 3.47; 95% CI, 2.26-5.33), rural residence (OR, 1.55; 95% CI, 1.05-2.30), and history of raw liver intake (OR, 8.52; 95% CI, 3.61-20.11) were significantly associated with Toxocara seropositivity. When subjects were divided into 3 groups using cutoff values base on weak positive and strong positive control optical densities (ODs), the ORs for peripheral blood eosinophilia and serum hyperIgEaemia were 0.31 (95% CI, 0.02-2.89) in the weakpositive group and 36.64 (95% CI, 11.73-111.42) in the strong positive group compared to the seronegative group. Similarly, ORs for the solid nodule with surrounding halo were 2.54 (95% CI, 0.60-10.84) in the weak positive group and 15.08 (95 CI 4.09-55.56) in the strong positive group compared to the seronegative group.ConclusionsThe study indicated that the Toxocara-seropositive rate obtained by using the current cutoff value based on ELISA was high in the asymptomatic population in Korea. The results of this study suggest that active toxocariasis may be more frequently seen in the Toxocara-strong positive group than in the Toxocara-weak positive group.

Background and PurposeEosinophilic granulomatosis with polyangiitis (EGPA) is a rare systemic small-vessel vasculitis accompanied by asthma, eosinophilia, and eosinophilic inflammation of various tissues including the peripheral nerves. This study investigated the clinical course and long-term outcomes of peripheral neuropathy in patients with EGPA.MethodsSeventy-one patients with physician-diagnosed EGPA were identified at Samsung Medical Center between January 1995 and April 2014. Sixty-one of these patients were followed-up for more than 1 year and received corticosteroid therapy with or without intravenous cyclophosphamide pulse therapy for 6 to 18 months. Medical records of the 61 patients including demographic data, clinical features, laboratory and pathological findings, treatments, and outcomes were reviewed.ResultsPeripheral neuropathy as a manifestation of EGPA was present in 46 (75%) of the 61 patients. The mean follow-up duration of the patients with neuropathy was 6.4 years (range 1.2–18.8 years). The scores on the neurological functional disability scale before and after the combination treatment with corticosteroid and cyclophosphamide were 2.43±0.86 and 0.54±0.95 (mean±SD; p<0.001), respectively. The peripheral neuropathy relapsed in one patient.ConclusionsThe long-term clinical outcome of peripheral neuropathy in patients with EGPA receiving initial corticosteroid and cyclophosphamide combination therapy was favorable with a very low relapse rate.

Recent advancements in deep learning for automated image processing and classification have accelerated many new applications for medical image analysis. However, most deep learning algorithms have been developed using reconstructed, human-interpretable medical images. While image reconstruction from raw sensor data is required for the creation of medical images, the reconstruction process only uses a partial representation of all the data acquired. Here, we report the development of a system to directly process raw computed tomography (CT) data in sinogram-space, bypassing the intermediary step of image reconstruction. Two classification tasks were evaluated for their feasibility of sinogram-space machine learning: body region identification and intracranial hemorrhage (ICH) detection. Our proposed SinoNet, a convolutional neural network optimized for interpreting sinograms, performed favorably compared to conventional reconstructed image-space-based systems for both tasks, regardless of scanning geometries in terms of projections or detectors. Further, SinoNet performed significantly better when using sparsely sampled sinograms than conventional networks operating in image-space. As a result, sinogram-space algorithms could be used in field settings for triage (presence of ICH), especially where low radiation dose is desired. These findings also demonstrate another strength of deep learning where it can analyze and interpret sinograms that are virtually impossible for human experts.

Despite the well-established impact of sex and sex hormones on bone structure and density, there has been limited description of sexual dimorphism in the hand and wrist in the literature. We developed a deep convolutional neural network (CNN) model to predict sex based on hand radiographs of children and adults aged between 5 and 70 years. Of the 1531 radiographs tested, the algorithm predicted sex correctly in 95.9% ( κ = 0.92) of the cases. Two human radiologists achieved 58% ( κ = 0.15) and 46% ( κ = − 0.07) accuracy. The class activation maps (CAM) showed that the model mostly focused on the 2nd and 3rd metacarpal base or thumb sesamoid in women, and distal radioulnar joint, distal radial physis and epiphysis, or 3rd metacarpophalangeal joint in men. The radiologists reviewed 70 cases (35 females and 35 males) labeled with sex along with heat maps generated by CAM, but they could not find any patterns that distinguish the two sexes. A small sample of patients ( n = 44) with sexual developmental disorders or transgender identity was selected for a preliminary exploration of application of the model. The model prediction agreed with phenotypic sex in only 77.8% ( κ = 0.54) of these cases. To the best of our knowledge, this is the first study that demonstrated a machine learning model to perform a task in which human experts could not fulfill.

BackgroundEffective surveillance of influenza requires a broad network of health care providers actively reporting cases of influenza-like illnesses and positive laboratory results. Not only is this traditional surveillance system costly to establish and maintain but there is also a time lag between a change in influenza activity and its detection. A new surveillance system that is both reliable and timely will help public health officials to effectively control an epidemic and mitigate the burden of the disease.ObjectiveThis study aimed to evaluate the use of parent-reported data of febrile illnesses in children submitted through the Fever Coach app in real-time surveillance of influenza activities.MethodsFever Coach is a mobile app designed to help parents and caregivers manage fever in young children, currently mainly serviced in South Korea. The app analyzes data entered by a caregiver and provides tailored information for care of the child based on the child’s age, sex, body weight, body temperature, and accompanying symptoms. Using the data submitted to the app during the 2016-2017 influenza season, we built a regression model that monitors influenza incidence for the 2017-2018 season and validated the model by comparing the predictions with the public influenza surveillance data from the Korea Centers for Disease Control and Prevention (KCDC).ResultsDuring the 2-year study period, 70,203 diagnosis data, including 7702 influenza reports, were submitted. There was a significant correlation between the influenza activity predicted by Fever Coach and that reported by KCDC (Spearman ρ=0.878; P<.001). Using this model, the influenza epidemic in the 2017-2018 season was detected 10 days before the epidemic alert announced by KCDC.ConclusionsThe Fever Coach app successfully collected data from 7.73% (207,699/2,686,580) of the target population by providing care instruction for febrile children. These data were used to develop a model that accurately estimated influenza activity measured by the central government agency using reports from sentinel facilities in the national surveillance network.