Surveillance Algorithms Research Articles

QoS guaranteed surveillance algorithms for directional wireless sensor networks

A directional wireless sensor network (DWSN) consists of a number of directional sensors. Many types of directional sensors have been widely used in constructing a wireless sensor network for IoT applications. These types of sensors include infrared sensors, microwave sensors, ultrasonic sensors, cameras, and radar. Compared with an omni-directional sensor, a directional sensor can achieve better performance because it can additionally report the direction of an intruder. Unfortunately, most existing barrier-coverage mechanisms adopt omni-directional sensor networks. They cannot be efficiently applied to DWSNs because the sensing area of each directional sensor is fan-shaped. This paper investigates the surveillance service problem which supports the surveillance quality of k-barrier coverage in DWSNs. Three algorithms, called BA, BTA and BRA, are proposed which aim at finding a maximal number of different defense barriers, each of which supports k-coverage. Using these algorithms, at least k directional sensors can detect intruders intending to cross the monitoring area. Performance analyses of the proposed algorithms are conducted in Section 5 to verify the performance improvement from a theoretical point of view. In the performance study, the proposed algorithms are compared with other existing algorithms. The experimental study shows that the proposed k-barrier coverage algorithm achieves similar performance with the optimal solution but has fewer control packets. Furthermore, the proposed BRA achieves better performance in terms of the numbers of control packets and constructed defense barriers, as compared with existing algorithms.

Implementation of a new algorithm resulting in improvements in accuracy and resolution of SEVIRI hotspot products

ABSTRACTActive wildfire detection, surveillance and mapping is an important application of satellite remote sensing. The Active Fire Monitoring (FIR) products, from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the Meteosat Second Generation (MSG) satellites, provide rapid-fire detection data every 5 to 15 over the European and African continents. However, the real world application of this high temporal frequency data is hindered due to the product spatial resolution of 3 3, thus limiting the application in fire surveillance and mapping activities. This letter implements a modified version of the Advanced Himawari-8 Imager – Fire Surveillance Algorithm (AHI-FSA) for SEVIRI with the aim of improving the spatial resolution of fire activity mapping. Initial results demonstrate the algorithm was able to improve the resolution of fire detection from 3 3 km to 1 1 km and simultaneously reduce the commission and omission errors by 25% and 16% respectively.

Abstract 5332: Prognostic value of aberrant promoter hypermethylation of tumor-related genes in early-stage head and neck cancer

Abstract Staging and pathological grading are useful, but imperfect predictors of recurrence in head and neck squamous cell carcinoma (HNSCC). Accordingly, molecular biomarkers that predict the risk of recurrence are necessary to improve clinical outcomes. The ability to distinguish between low- and high-risk HNSCCs at an early stage may reduce follow-up costs. We hypothesized that the quantitative methylation specific PCR (Q-MSP) assay could be used to define patterns of DNA methylation that differentiate low- and high-risk HNSCCs. The methylation statuses of the promoters of 11 tumor-related genes (p16, RASSF1A, E-cadherin, H-cadherin, MGMT, DAPK, DCC, COL1A2, TAC1, SST, and GALR1) were analyzed in 133 HNSCC cases using quantitative methylation-specific PCR. Frequent methylation of p16 (44%), RASSF1A (18%), E-cadherin (53%), H-cadherin (35%), MGMT (35%), DAPK (53%), DCC (42%), COL1A2 (44%), TAC1 (61%), SST (64%), and GALR1 (44%) in HNSCC were detected. Disease-free survival was lower in patients with 6-11 methylated genes than in those with 0-5 methylated genes (log-rank test, P = 0.001). In a multivariate Cox proportional hazards analysis, the methylation of E-cadherin, COL1A2, TAC1, and GALR1 was associated with poor survival, with hazard ratios of 4.474 (95% CI, 1.241-16.124). In a joint analysis of these four genes, patients with 2-4 methylated genes had a significantly lower survival rate than those with 0-1 methylated genes in early-stage HNSCC. Importantly, the methylation of some genes was closely related to poor prognosis in early-stage HNSCC, providing strong evidence that these hypermethylated genes are valuable biomarkers for prognostic evaluation. In conclusion, the methylation profiles of E-cadherin, COL1A2, TAC1, and GALR1 were the most powerful combination for predicting early-stage HNSCC. This demonstrates that molecular stratification may predict cancer progression. These findings can benefit HNSCC screening and surveillance algorithms. Although our study was retrospective, was conducted at a single institution, and the number of patients was small, it serves as a platform to establish optimal therapeutic strategies for early-stage HNSCC. Citation Format: Atsushi Imai, Kiyoshi Misawa, Daiki Mochizuki, Hiroyuki Mineta, Takeharu Kanazawa. Prognostic value of aberrant promoter hypermethylation of tumor-related genes in early-stage head and neck cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5332.

Border Surveillance With WSN Systems in a Distributed Manner

Border surveillance is an application of wireless sensor networks (WSNs) and it is mainly used for security purpose. The present issue of implementing this application is a barrier coverage in WSNs. Due to energy constraints, minimizing energy consumption of sensors is a critical challenge. This paper presents a new distributed approach to the border surveillance in WSNs in order to maximize the number of barriers and minimize energy consumption. We use a learning automata scheme in our approach to solve this problem. We present distributed border surveillance (DBS) algorithm that aims to find the minimum possible number of nodes in each barrier to monitor the network borders. In DBS approach, learning automaton assists to find the best nodes to assure barrier coverage at any moment. The algorithm was implemented through computer simulations and compared to the existing competing algorithms. The simulation results show that the DBS scheme outperformed some state-of-art barrier-coverage algorithms.

FluHMM: A simple and flexible Bayesian algorithm for sentinel influenza surveillance and outbreak detection.

Timely detection of the seasonal influenza epidemic is important for public health action. We introduce FluHMM, a simple but flexible Bayesian algorithm to detect and monitor the seasonal epidemic on sentinel surveillance data. No comparable historical data are required for its use. FluHMM segments a typical influenza surveillance season into five distinct phases with clear interpretation (pre-epidemic, epidemic growth, epidemic plateau, epidemic decline and post-epidemic) and provides the posterior probability of being at each phase for every week in the period under surveillance, given the available data. An alert can be raised when the probability that the epidemic has started exceeds a given threshold. An accompanying R package facilitates the application of this method in public health practice. We apply FluHMM on 12 seasons of sentinel surveillance data from Greece, and show that it achieves very good sensitivity, timeliness and perfect specificity, thereby demonstrating its usefulness. We further discuss advantages and limitations of the method, providing suggestions on how to apply it and highlighting potential future extensions such as with integrating multiple surveillance data streams.

A radar-based centroid tracking algorithm for severe weather surveillance: identifying split/merge processes in convective systems

Nowcasting tools using radar data are essential when researching severe weather phenomena. Identifying thunderstorms and characterising their properties are usually the main steps to follow in order to make a good short-term forecast. These analyses are normally used to feed early warning systems in many countries, helping to reduce the impact of severe weather. Over the last few decades, research in this meteorological field has increased significantly, defining different methodologies that have worked well for many severe events. Most of these methodologies are based on a prior 3D identification of the thunderstorms in question, by applying intensity and size thresholds and obtaining their characteristics, allowing their centroids to be tracked. In some cases, the thresholds for identification and the distance established to track the centroid are too restrictive, which results in the incorrect identification of individual cells in multicell systems, or even means that the algorithm is unable to distinguish when an anomalous movement takes place. This paper presents a new type of 3D cell identification and tracking, based on the algorithm developed by Rigo and Llasat (2004). This last algorithm is currently used in the Meteorological Service of Catalonia (SMC), and consists of a nowcasting methodology based on centroid tracking. In the new version, a new two-step methodology to construct 3D radar cells has been proposed. A double reflectivity thresholding method plus an overlapping technique have been applied for a better definition of the convective structures. This makes it possible to identify possible changing processes such as splitting or merging within the same thunderstorm. Furthermore, a dynamic distance threshold technique has been adopted to correctly track cells with anomalous movements. An in-depth study has been carried out for a severe weather case in which a multicell system presented a complex merging and splitting process that was correctly forecasted using the proposed algorithm.

Evaluation of Syndrome Algorithms for Detecting Pneumonia Emergency Department Visits

ObjectiveTo validate and improve the syndromic algorithm used to describe pneumonia emergency department (ED) visit trends in New York City (NYC).IntroductionThe NYC Department of Health and Mental Hygiene (DOHMH) uses ED syndromic surveillance to monitor near real-time trends in pneumonia visits. The original pneumonia algorithm was developed based on ED chief complaints, and more recently was modified following a legionella outbreak in NYC. In 2016, syndromic data was matched to New York State all payer database (SPARCS) for 2010 through 2015. We leveraged this matched dataset to validate ED visits identified by our pneumonia algorithm and suggest improvements. An effective algorithm for tracking trends in pneumonia could provide critical information to inform and facilitate public health decision-making.MethodsThe DOHMH syndromic surveillance system includes daily ED data from 53 NYC hospitals. Most syndrome algorithms rely solely on chief complaint, which has historically been reported more consistently than discharge diagnosis. For this analysis, the validation dataset was restricted to matched visits with consistent age (plus or minus two years) and sex between the syndromic and SPARCS datasets.The original pneumonia algorithm used a basic text search function to identify any mention of ICD-9-CM and ICD-10-CM diagnosis codes indicating pneumonia or key words “PNEUMON” or “MONIA” within the chief complaint only. The updated algorithm additionally searches the chief complaint for any mention of key words specific to legionella (“LEGIONA”, “LEGIONN”, “LEGIONE”) and also searches for pneumonia ICD codes in the discharge diagnosis field. Syndrome sensitivity and positive predictive value (PPV) were evaluated by comparing visits identified by each algorithm to visits identified by billing diagnosis codes. A true SPARCS pneumonia ED visit was defined to contain an admitting or principal diagnosis billing code indicating pneumonia.Alternate algorithms were created using regular expressions, which allowed for more flexible and accurate pattern matching. The algorithms were further revised based on additional inclusion and exclusion key words identified using the validation dataset.ResultsBetween 2010 and 2015, there were a total of 204,101 true pneumonia visits based on the SPARCS billing records. Evaluation of the original algorithm found a sensitivity of 15.6% (31,771/204,101) and a PPV of 55.6% (31,771/57,180). Over the same time period, syndromic surveillance identified a total of 127,560 pneumonia visits using the updated algorithm; 86,590 of the 127,560 syndromic cases identified were determined to be a true visit based on the billing diagnosis codes, resulting in an algorithm sensitivity of 42.4% and PPV of 67.9%. Of the 127,560 cases identified by the updated algorithm, 19 cases were classified as a pneumonia visit solely due to the presence of legionella key words in the chief complaint. Regular expression usage as opposed to the basic text search on the updated algorithm found similar sensitivity (42.4%, 86,561/204,101) and PPV (68.0%, 86,561/127,238).Among all true pneumonia visits with a non-blank discharge diagnosis field, 65.3% (68,001/104,223) had mention of a pneumonia diagnosis code. Use of the discharge diagnosis code field in addition to the chief complaint found the algorithm to be almost three times more sensitive and 1.2 times greater in PPV than an algorithm without use of discharge diagnosis. Seasonal trends captured with and without use of discharge diagnosis were both similar to the true pneumonia trend indicated by SPARCS.Among the 117,540 pneumonia cases missed by the updated algorithm, 58.6% had fewer than three words in the chief complaint. With the most popular key words among the missed cases being highly non-specific (i.e., “fever”, “cough”, “pain”), inclusion of these key words in addition to regular expression and discharge diagnosis field usage elevated algorithm sensitivity at a severe cost to the PPV. Including “fever” in the list of pneumonia key words resulted in a sensitivity of 56.5% (115,280/204,101) and a PPV of 9.0% (115,280/1,282,342), while addition of the key word combination “fever” and “cough” led to a sensitivity of 46.7% (95,264/204,101) and a PPV of 29.8% (95,264/319,876).As we were unable to identify novel key word indicators that were good markers for pneumonia events, regular expression search functionality was the best improvement to the pneumonia syndrome algorithm. This revised, new algorithm maintained sensitivity (42.4%, 86,561/204,101) and provided slight improvements to PPV (68.0%, 86,561/127,219).However, performance of the updated algorithm varied across age groups. The algorithm was most effective in identifying younger cases (43.9% sensitivity, 80.4% PPV for those 17 years and younger), while it performed the worst among those 65 years and older (39.6% sensitivity, 58.7% PPV).ConclusionsBased on our evaluation of the pneumonia syndromic surveillance algorithm, we found that search of the discharge diagnosis field greatly improved algorithm sensitivity and PPV and usage of regular expressions increased PPV slightly. Including additional words possibly indicating pneumonia did not substantially improve sensitivity or PPV. However, integration of the ED chief complaint triage notes which are not currently utilized could further enhance the effectiveness of the pneumonia syndrome algorithm and better characterize daily pneumonia trends in NYC.

Rolling element bearings localized fault diagnosis using signal differencing and median filtration

With the increase complexity of bearings’ processing algorithms and the growing trend of using computationally demanding algorithms, it is advantageous to provide analysts with a simple to use and implement algorithm. In this spirit, this paper combines simple functions to provide machine condition analysts with the capacity to diagnose bearing faults without all the complexity and jargon that comes with existing methods. The paper proposes a simplified surveillance and diagnostic algorithm for diagnosing localized faults in rolling element bearings using measured raw vibration signals. The proposed algorithm is based on analyzing the frequency content obtained from applying a median filter on the squared derivative signal (first or higher derivatives) of the vibration signal. The combination of signal differencing and median filters provides a squared envelope signal, which can be used directly to diagnose faults. Signal differencing gives a measure of jerk forces and lifts the high frequency content of the signal. To select the optimum order of differentiation, Kurtosis and maximum correlated kurtosis (MCK) are proposed. Median filter usage represents a better alternative of normal low pass filtration. This completely suppresses impulses with large magnitudes, which may interfere with the diagnosis. The length of the median filter (odd number 3, 5, 7 etc.) is selected as such to include the first 10 harmonics of the defect frequency. Simulated signals are used to demonstrate the efficiency of the proposed algorithm and give insights into the choices of the differentiation and smoothening orders. The proposed processing algorithm gives a first measure (surveillance) for detecting localized faults in rolling element bearings in a very simple way and can be employed in online learning and diagnosis systems. Results obtained from applying the algorithm on complex vibration signals from two types of gearboxes are compared with a well-established semi-automated technique with good correspondence.

Actionable Locoregional Relapses after Therapy of Localized Esophageal Cancer: Insights from a Large Cohort

Objective: The goal of surveillance after therapy of localized esophageal cancer (LEC) is to identify actionable relapses amenable to salvage; however, the current surveillance algorithms are not optimized. We report on a large cohort of LEC patients with actionable locoregional relapses (LRRs). Methods: Between 2000 and 2013, 127 (denominator = 752) patients with actionable LRR were identified. Histologic/cytologic confirmation was the gold standard. All surveillance tools (imaging, endoscopy, fine needle aspiration) were assessed. Results: Most patients were men (89%), had adenocarcinoma (79%), and had no new symptoms (72%) when diagnosed with LRR. In trimodality patients, endoscopic confirmation of positron emission tomography-computed tomography-suspected LRR occurred in only 44%, and 56% required additional tools (e.g., fine needle aspiration). Alternatively, in bimodality patients, endoscopy confirmed LRRs in 81%. Trimodality patients had a higher risk of subsequent LRR/distant metastases after the first LRR than the bimodality patients (p = 0.03). In all patients, 78% of the subsequent relapses were distant. For patients who were salvaged, survival was significantly prolonged (50.6 vs. 25.1 months, p < 0.01). Conclusions: Patients live longer after successful salvage of the LRR than if salvage is not possible. After LRR, patients have a high risk of subsequent distant metastasis and whether the second relapse is local or distant, survival is uniformly poor.

A system for the generation of synthetic Wide Area Aerial surveillance imagery

The development, benchmarking and validation of aerial Persistent Surveillance (PS) algorithms requires access to specialist Wide Area Aerial Surveillance (WAAS) datasets. Such datasets are difficult to obtain and are often extremely large both in spatial resolution and temporal duration. This paper outlines an approach to the simulation of complex urban environments and demonstrates the viability of using this approach for the generation of simulated sensor data, corresponding to the use of wide area imaging systems for surveillance and reconnaissance applications. This provides a cost-effective method to generate datasets for vehicle tracking algorithms and anomaly detection methods. The system fuses the Simulation of Urban Mobility (SUMO) traffic simulator with a MATLAB controller and an image generator to create scenes containing uninterrupted door-to-door journeys across large areas of the urban environment. This ‘pattern-of-life’ approach provides three-dimensional visual information with natural movement and traffic flows. This can then be used to provide simulated sensor measurements (e.g. visual band and infrared video imagery) and automatic access to ground-truth data for the evaluation of multi-target tracking systems.

Robustness Analysis of Pedestrian Detectors for Surveillance

To obtain effective pedestrian detection results in surveillance video, there have been many methods proposed to handle the problems from severe occlusion, pose variation, clutter background, \emph{etc}. Besides detection accuracy, a robust surveillance video system should be stable to video quality degradation by network transmission, environment variation, etc. In this study, we conduct the research on the robustness of pedestrian detection algorithms to video quality degradation. The main contribution of this work includes the following three aspects. First, a large-scale Distorted Surveillance Video Data Set (DSurVD) is constructed from high-quality video sequences and their corresponding distorted versions. Second, we design a method to evaluate detection stability and a robustness measure called Robustness Quadrangle, which can be adopted to visualize detection accuracy of pedestrian detection algorithms on high-quality video sequences and stability with video quality degradation. Third, the robustness of seven existing pedestrian detection algorithms is evaluated by the built DSurVD. Experimental results show that the robustness can be further improved for existing pedestrian detection algorithms. Additionally, we provide much in-depth discussion on how different distortion types influence the performance of pedestrian detection algorithms, which is important to design effective pedestrian detection algorithms for surveillance. The DSurVD data set can be download from BaiduYunDisk, https://pan.baidu.com/s/1I9Kqj8rmubOYu7bkBfkUpA, Password: lqmc

A Cortical Learning Movement Classification Algorithm for Video Surveillance

A Cortical Learning Movement Classification Algorithm for Video Surveillance

Cone-beam computed tomography (CBCT) for adaptive image guided head and neck radiation therapy

Background: Adaptive (ART) and image guided radiation therapy (IGRT) can improve target coverage and reduce unnecessary irradiation of organs at risk (OAR). The purpose of this study is to report the results of using mid-course imaging and a novel workflow with cone-beam computed tomography (CBCT) surveillance of dose to OAR to trigger adaptive replanning in head and neck radiotherapy.Material and methods: Impact of radiation therapist (RTT) managed match protocol and mid-course imaging was assessed in two cohorts of consecutive patients receiving RT to the head and neck region, using computed tomography (CT) and CBCT-based dose verification respectively. In the CBCT cohort, patients at high risk of xerostomia received weekly dose surveillance, while low-risk patients received a mid-course CBCT review. For weekly surveillance, predicted total doses to parotid glands, spinal cord and brainstem were calculated. If predicted mean dose to parotid glands increased by >2 Gy or constraints to brainstem or spinal cord were exceeded, replanning was performed.Results: None had replanning triggered by mid-course imaging. In the CBCT cohort, weekly surveillance of 40 patients yielded minimal reduction in mean dose to parotid glands of 0.65 Gy (range0.4–1 Gy ) for three patients. Patients were surveilled averagely 4.5 times during treatment. Time consumption per CBCT/week was 22 min (range 17–38). Number of patients needed to see to achieve any dose reduction to parotid glands was 13 or the equivalent of 22 working-hours.Conclusion: The tested dose surveillance algorithm resulted in a minimal dose reduction ( ≤1 Gy) to parotid glands for three of 40 patients. The proposed algorithm and workflow is thus not sustainable. Mid-course dose verification did not provide added benefit and can be safely omitted in the presence of closely monitored daily IGRT. Daily image guidance and match protocol is a safe and efficient method for identifying patients requiring adaptive replanning.

Extensively drug resistant tuberculosis in Mali: a case report

BackgroundDrug resistant tuberculosis presents a major public health challenge.Case presentationWe present here the first two patients diagnosed with extensively drug resistant tuberculosis in Bamako, Mali. Genotypic findings suggest possible nosocomial transmission from the first patient to the second one, resulting in superinfection of the second patient. After being diagnosed with extensively drug resistant tuberculosis in August 2016, the patients only started receiving appropriate treatment 10 months later.ConclusionThe identification of these patients highlights the need for improved diagnostic and treatment algorithms for better surveillance and management of drug resistance in Mali. In the interest of these as well as future patients suffering from resistant tuberculosis, all steps recommended for programmatic management of drug resistant tuberculosis must be urgently prioritized in order to strengthen the multidrug resistant tuberculosis program.

The Natural Growth Rate of Residual Juvenile Angiofibroma.

Objectives Examine the postoperative growth rate of residual juvenile angiofibroma (JA) in a large series of patients relative to pediatric growth parameters and other prognostic factors. Establish an algorithm for postoperative surveillance of patients with JA. Design Retrospective case series. Setting Tertiary referral academic center. Participants Pediatric patients undergoing surgical resection of JA between September 2005 and June 2015. Main Outcome Measures Postoperative recurrence and tumor growth rates. Results Thirty-eight patients were identified with a mean follow-up of 24.1 months. Sixty-eight percent (26/34) of patients achieved gross total resection, and 32% (12/38) had persistent postoperative disease. Of those with postoperative residual tumor, all had preoperative skull base involvement and residual blood supply from the internal carotid artery following embolization. Sixty-seven percent (8/12) of patients with postoperative residual tumor demonstrated radiographic stability of disease with scans being completed approximately every 6 months. Radiographic rates of disease progression ranged from 4.1 to 9.22 mm/year. Conclusions Patients with preoperative skull base involvement and residual vascularity following preoperative embolization are more likely to have residual postoperative disease, and a longer postoperative follow-up is warranted in these patients. Progression of residual disease occurred approximately 7.5 months postoperatively. Younger patients may be more likely to need further intervention for postoperative residual disease, and postoperative imaging at 6-month intervals appears appropriate.

US LI-RADS: ultrasound liver imaging reporting and data system for screening and surveillance of hepatocellular carcinoma.

Ultrasound is the most widely used imaging tool for hepatocellular carcinoma (HCC) screening and surveillance. Until now, this method has lacked standardized guidelines for interpretation, reporting, and management recommendations [1-5]. To address this need, the American College of Radiology (ACR) has developed the Ultrasound Liver Imaging Reporting and Data System (US LI-RADS) algorithm. The proposed algorithm has two components: detection scores and visualization scores. The detection score guides management and has three categories: US-1 Negative, US-2 Subthreshold, and US-3 Positive. The visualization score informs the expected sensitivity of the ultrasound examination and also has three categories: Visualization A: No or minimal limitations; Visualization B: Moderate limitations; and Visualization C: Severe limitations. Standardization in ultrasound utilization, reporting, and management in high-risk individuals has the capacity to improve communication with patients and referring physicians, unify screening and surveillance algorithms, impact outcomes, and supply quantitative data for future research.

Fast and robust occluded face detection in ATM surveillance

Crimes with respect to ATMs (Automatic Teller Machines) have attracted more and more attention, where criminals deliberately cover their faces in order to avoid being identified. This paper proposes a fast and robust face occlusion detection algorithm for ATM surveillance, which is demonstrated to be effective and efficient to handle arbitrarily occluded faces. In this algorithm, we innovatively propose to make use of the Omega shape formed by the head and shoulder of the person for head localization to tackle severe face occlusion. For this purpose, we first construct a novel energy function for elliptical head contour detection. Then, we develop a fast and robust head tracking algorithm, which utilizes the gradient and shape cues in a Bayesian framework. Lastly, to verify whether a detected head is occluded or not, we propose to fuse information from both skin color and face structure using the AdaBoost algorithm. Experimental results on real world data show that our proposed algorithm can achieve 98.64% accuracy on face detection and 98.56% accuracy on face occlusion detection, even though there are severe occlusions in faces, at a speed of up to 12 frames per second.

Hepatocellular carcinoma imaging systems: why they exist, how they have evolved, and how they differ.

Over the past 16years, several scientific organizations have proposed systems that incorporate imaging for surveillance, diagnosis, staging, treatment, and monitoring of treatment response of hepatocellular carcinoma (HCC). These systems are needed to standardize the acquisition, interpretation, and reporting of liver imaging examinations; help differentiate benign from malignant observations; improve consistency between radiologists; and provide guidance for management of HCC. This review article discusses the historical evolution of HCC imaging systems. We indicate the features differentiating these systems, including target population, screening and surveillance algorithm, diagnostic imaging modalities, diagnostic scope, expertise and technical requirements, terminology, major and ancillary imaging features, staging and transplant eligibility, and assessment of treatment response. We highlight the potential benefits of unifying the systems, which we anticipate will enable sharing, pooling, and meta-analysis of data; facilitate multi-center trials; and accelerate dissemination of knowledge.

Automated surveillance of healthcare-associated infections: state of the art.

This review describes recent advances in the field of automated surveillance of healthcare-associated infections (HAIs), with a focus on data sources and the development of semiautomated or fully automated algorithms. The availability of high-quality data in electronic health records and a well-designed information technology (IT) infrastructure to access these data are indispensable for successful implementation of automated HAI surveillance. Previous studies have demonstrated that reliance on stand-alone administrative data is generally unsuited as sole case-finding strategy. Recent attempts to combine multiple administrative and clinical data sources in algorithms yielded more reliable results. Current surveillance practices are mostly limited to single healthcare facilities, but future linkage of multiple databases in a network may allow interfacility surveillance. Although prior surveillance algorithms were often straightforward decision trees based on structured data, recent studies have used a wide variety of techniques for case-finding, including logistic regression and various machine learning methods. In the future, natural language processing may enable the use of unstructured narrative data. Developments in healthcare IT are rapidly changing the landscape of HAI surveillance. The electronic availability and incorporation of routine care data in surveillance algorithms enhances the reliability, efficiency and standardization of surveillance practices.

Playing the odds: lung cancer surveillance after curative surgery.

After 'curative' resection, many patients are still at risk for further lung cancer, either as a recurrence or a new metachronous primary. In theory, close follow-up should improve survival by catching relapse early - but in reality, many experts feel that surveillance for recurrence is of uncertain value. In this article, we explore the reasons behind the controversy, what the current guidelines recommend, and what future solutions are in development that may ultimately resolve this debate. Although postoperative surveillance for a new lung cancer may impart a survival advantage, this benefit does not appear to extend to the phenomenon of recurrence. Nevertheless, close radiographic follow-up after curative resection is still recommended by most professional societies, with more frequent scanning in the first 2 years, and then annual screening thereafter. Given the radiation risk, however, low-dose and minimal-dose computed tomography options are under investigation, as well as timing scans around expected peaks of recurrence rather than a set schedule. Applying the same surveillance algorithm to all lung cancer patients after curative resection may not be cost-effective or reasonable, especially if there is no demonstrable mortality benefit. Therefore, future research should focus on finding safer nonradiographic screening options, such as blood or breath biomarkers, or developing nomograms for predicting which patients will relapse and require closer follow-up. Ultimately, however, better tools for surveillance may be moot until we develop better treatment options for lung cancer recurrence.