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Background: Missed findings in chest X-ray interpretation are common and can have serious consequences. Methods: Our study included 2407 chest radiographs (CXRs) acquired at three Indian and five US sites. To identify CXRs reported as normal, we used a proprietary radiology report search engine based on natural language processing (mPower, Nuance). Two thoracic radiologists reviewed all CXRs and recorded the presence and clinical significance of abnormal findings on a 5-point scale (1—not important; 5—critical importance). All CXRs were processed with the AI model (Qure.ai) and outputs were recorded for the presence of findings. Data were analyzed to obtain area under the ROC curve (AUC). Results: Of 410 CXRs (410/2407, 18.9%) with unreported/missed findings, 312 (312/410, 76.1%) findings were clinically important: pulmonary nodules (n = 157), consolidation (60), linear opacities (37), mediastinal widening (21), hilar enlargement (17), pleural effusions (11), rib fractures (6) and pneumothoraces (3). AI detected 69 missed findings (69/131, 53%) with an AUC of up to 0.935. The AI model was generalizable across different sites, geographic locations, patient genders and age groups. Conclusion: A substantial number of important CXR findings are missed; the AI model can help to identify and reduce the frequency of important missed findings in a generalizable manner.

Objectives The primary objective of the study was to assess the feasibility and sustainability of the implementation of the point of care quality improvement (POCQI) methodology for improving the quality of neonatal care at the level 2 special newborn care unit (SNCU). Additional objective was to evaluate the effectiveness of the quality improvement (QI) and preterm baby package training model. Methods This study was conducted in a level-II SNCU. The study period was divided into baseline; intervention and sustenance phases. The primary outcome i.e., feasibility was defined as completion of training for 80% or more health care professionals (HCPs) through workshops, their attendance in subsequent review meetings and, successful accomplishment of at least two plan-do-study-act (PDSA) cycles in each project. Results Of the total, 1217 neonates were enrolled during the 14 mo study period; 80 neonates in the baseline, 1019 in intervention and 118 in sustenance phases. Feasibility of training was achieved within a month of initiation of intervention phase; 22/24 (92%) nurses and 14/15 (93%) doctors attended the meetings. The outcomes of individual projects suggested an improvement in proportion of neonates being given exclusive breast milk on day 5 (22.8% to 78%); mean difference (95% CI) [55.2 (46.5 to 63.9)]. Neonates on any antibiotics declined, proportion of any enteral feeds on day one and duration of kangaroo mother care (KMC) increased. Proportion of neonates receiving intravenous fluids during phototherapy decreased. Conclusions The present study demonstrates the feasibility, sustainability, and effectiveness of a facility-team-driven QI approach augmented with capacity building and post-training supportive supervision.

Remote sensing exploitation house and mobile sensors has shown to be effective in providing immediate response and help throughout disaster recovery[4]. Since it will limit search and rescue personnel, a close to period of time diagnosing of disaster-affected places is efficient. Literature Review Object-based classifications, pattern recognition and supervised area unit a number of the techniques wont to retrieve data concerning natural disaster injury. mobile pictures non heritable through remote sense techniques area unit wont to extract injury data concerning buildings [2], [3], [12]-[15]. Pre- and post-event information area unit employed in strategies. the benefits of approaches that use solely post-event imagination, whereas consider the trade-off between preciseness and timely delivery, area unit that destruction extractions is monitored despite the dearth of reference information merit quick initial injury analysis and the swift acknowledgement. [...]with the supply of pre and post pictures, amendment detection could be a typical technique employed in science. options that account for texture, edges, and models area unit extracted ,trained by straightforward delivery. Gaussian Noise, if present, is eliminated mistreatment adaptational filtering, that is a lot of selective than a linear filter, once evaluating the bar chart of the image into account. Since ancient pel-based classification ways square measure ineffective for newer high-resolution pictures thanks to their sophistication, elaborate data material, [4], [19], we have a tendency to used a classification methodology that accounts for native patterns likewise as texture options within