Artificial intelligence-supported lung cancer detection by multi-institutional readers with multi-vendor chest radiographs: a retrospective clinical validation study

Background We investigated the performance improvement of physicians with varying levels of chest radiology experience when using a commercially available artificial intelligence (AI)-based computer-assisted detection (CAD) software to detect lung cancer nodules on chest radiographs from multiple vendors. Methods Chest radiographs and their corresponding chest CT were retrospectively collected from one institution between July 2017 and June 2018. Two author radiologists annotated pathologically proven lung cancer nodules on the chest radiographs while referencing CT. Eighteen readers (nine general physicians and nine radiologists) from nine institutions interpreted the chest radiographs. The readers interpreted the radiographs alone and then reinterpreted them referencing the CAD output. Suspected nodules were enclosed with a bounding box. These bounding boxes were judged correct if there was significant overlap with the ground truth, specifically, if the intersection over union was 0.3 or higher. The sensitivity, specificity, accuracy, PPV, and NPV of the readers’ assessments were calculated. Results In total, 312 chest radiographs were collected as a test dataset, including 59 malignant images (59 nodules of lung cancer) and 253 normal images. The model provided a modest boost to the reader’s sensitivity, particularly helping general physicians. The performance of general physicians was improved from 0.47 to 0.60 for sensitivity, from 0.96 to 0.97 for specificity, from 0.87 to 0.90 for accuracy, from 0.75 to 0.82 for PPV, and from 0.89 to 0.91 for NPV while the performance of radiologists was improved from 0.51 to 0.60 for sensitivity, from 0.96 to 0.96 for specificity, from 0.87 to 0.90 for accuracy, from 0.76 to 0.80 for PPV, and from 0.89 to 0.91 for NPV. The overall increase in the ratios of sensitivity, specificity, accuracy, PPV, and NPV were 1.22 (1.14–1.30), 1.00 (1.00–1.01), 1.03 (1.02–1.04), 1.07 (1.03–1.11), and 1.02 (1.01–1.03) by using the CAD, respectively. Conclusion The AI-based CAD was able to improve the ability of physicians to detect nodules of lung cancer in chest radiographs. The use of a CAD model can indicate regions physicians may have overlooked during their initial assessment.

Risk factors and preventive strategies for unintentionally retained surgical sharps: a systematic review

Background A retained surgical item (RSI) is defined as a never-event and can have drastic consequences on patient, provider, and hospital. However, despite increased efforts, RSI events remain the number one sentinel event each year. Hard foreign bodies (e.g. surgical sharps) have experienced a relative increase in total RSI events over the past decade. Despite this, there is a lack of literature directed towards this category of RSI event. Here we provide a systematic review that focuses on hard RSIs and their unique challenges, impact, and strategies for prevention and management. Methods Multiple systematic reviews on hard RSI events were performed and reported using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and AMSTAR (Assessing the methodological quality of systematic reviews) guidelines. Database searches were limited to the last 10 years and included surgical “sharps,” a term encompassing needles, blades, instruments, wires, and fragments. Separate systematic review was performed for each subset of “sharps”. Reviewers applied reciprocal synthesis and refutational synthesis to summarize the evidence and create a qualitative overview. Results Increased vigilance and improved counting are not enough to eliminate hard RSI events. The accurate reporting of all RSI events and near miss events is a critical step in determining ways to prevent RSI events. The implementation of new technologies, such as barcode or RFID labelling, has been shown to improve patient safety, patient outcomes, and to reduce costs associated with retained soft items, while magnetic retrieval devices, sharp detectors and computer-assisted detection systems appear to be promising tools for increasing the success of metallic RSI recovery. Conclusion The entire healthcare system is negatively impacted by a RSI event. A proactive multimodal approach that focuses on improving team communication and institutional support system, standardizing reports and implementing new technologies is the most effective way to improve the management and prevention of RSI events.

Computer-Assisted Detection of Cemento-Enamel Junction in Intraoral Ultrasonographs

The cemento-enamel junction (CEJ) is an important reference point for various clinical measurements in oral health assessment. Identifying CEJ in ultrasound images is a challenging task for dentists. In this study, a computer-assisted detection method is proposed to identify the CEJ in ultrasound images, based on the curvature change of the junction outlining the upper edge of the enamel and cementum at the cementum–enamel intersection. The technique consists of image preprocessing steps for image enhancement, segmentation, and edge detection to locate the boundary of the enamel and cementum. The effects of the image preprocessing and the sizes of the bounding boxes enclosing the CEJ were studied. For validation, the algorithm was applied to 120 images acquired from human volunteers. The mean difference of the best performance between the proposed method and the two raters’ measurements was an average of 0.25 mm with reliability ≥ 0.98. The proposed method has the potential to assist dental professionals in CEJ identification on ultrasonographs to provide better patient care.

Diagnostic Breast Cancer Image Data Classification using CNN

Breast cancer has been dangerous form of cancer. In this report, we use a convolutional neural network to scan and separate infected cells.In this we diagnose if its benign or malignant cancer bulk using computer assisted detection(CAD). The productivity of open CAD has always been inadequate. Here, we use a deep CNN-based content detection method.We create narrower and broader images of histology patches with cell and tumour attributes. CNN constitutes unorganized data specifically for image data which has been said to be thriving in the area of image recognition .We use highly interconnected layer first cnn, in which those layers are incorporated before the first convolutional layer, since CNN does not support data sets.

Computer-assisted detection and classification of misinformation about climate change

A growing body of scholarship investigates the role of misinformation in shaping the debate on climate change. Our research builds on and extends this literature by 1) developing and validating a comprehensive taxonomy of climate misinformation, 2) conducting the largest content analysis to date on contrarian claims, 3) developing a computational model to accurately detect specific claims, and 4) drawing on an extensive corpus from conservative think-tank (CTTs) websites and contrarian blogs to construct a detailed history of misinformation over the past 20 years. Our study finds that climate misinformation produced by CTTs and contrarian blogs has focused on attacking the integrity of climate science and scientists and, increasingly, has challenged climate policy and renewable energy. We further demonstrate the utility of our approach by exploring the influence of corporate and foundation funding on the production and dissemination of specific contrarian claims.