GastroVision: A Multi-class Endoscopy Image Dataset for Computer Aided Gastrointestinal Disease Detection

Integrating real-time artificial intelligence (AI) systems in clinical practices faces challenges such as scalability and acceptance. These challenges include data availability, biased outcomes, data quality, lack of transparency, and underperformance on unseen datasets from different distributions. The scarcity of large-scale, precisely labeled, and diverse datasets are the major challenge for clinical integration. This scarcity is also due to the legal restrictions and extensive manual efforts required for accurate annotations from clinicians. To address these challenges, we present \textit{GastroVision}, a multi-center open-access gastrointestinal (GI) endoscopy dataset that includes different anatomical landmarks, pathological abnormalities, polyp removal cases and normal findings (a total of 27 classes) from the GI tract. The dataset comprises 8,000 images acquired from B{\ae}rum Hospital in Norway and Karolinska University Hospital in Sweden and was annotated and verified by experienced GI endoscopists. Furthermore, we validate the significance of our dataset with extensive benchmarking based on the popular deep learning based baseline models. We believe our dataset can facilitate the development of AI-based algorithms for GI disease detection and classification. Our dataset is available at \url{https://osf.io/84e7f/}

An objective validation of polyp and instrument segmentation methods in colonoscopy through Medico 2020 polyp segmentation and MedAI 2021 transparency challenges

Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps. However, detecting polyps during the live examination can be challenging due to various factors such as variation of skills and experience among the endoscopists, lack of attentiveness, and fatigue leading to a high polyp miss-rate. Deep learning has emerged as a promising solution to this challenge as it can assist endoscopists in detecting and classifying overlooked polyps and abnormalities in real time. In addition to the algorithm's accuracy, transparency and interpretability are crucial to explaining the whys and hows of the algorithm's prediction. Further, most algorithms are developed in private data, closed source, or proprietary software, and methods lack reproducibility. Therefore, to promote the development of efficient and transparent methods, we have organized the "Medico automatic polyp segmentation (Medico 2020)" and "MedAI: Transparency in Medical Image Segmentation (MedAI 2021)" competitions. We present a comprehensive summary and analyze each contribution, highlight the strength of the best-performing methods, and discuss the possibility of clinical translations of such methods into the clinic. For the transparency task, a multi-disciplinary team, including expert gastroenterologists, accessed each submission and evaluated the team based on open-source practices, failure case analysis, ablation studies, usability and understandability of evaluations to gain a deeper understanding of the models' credibility for clinical deployment. Through the comprehensive analysis of the challenge, we not only highlight the advancements in polyp and surgical instrument segmentation but also encourage qualitative evaluation for building more transparent and understandable AI-based colonoscopy systems

Spatial Heterogeneity in Tropospheric Column Ozone over the Indian Subcontinent: Long-Term Climatology and Possible Association with Natural and Anthropogenic Activities

Monthly averaged tropospheric ozone residual (TOR) data from TOMS and OMI during the period 1979–2009 are used to study the spatial distribution of tropospheric column ozone within the landmass of the Indian subcontinent, the Tibetan plateau in the north and the Bay of Bengal in the south. The climatological mean shows seasonal maxima in spring and minima in winter in all the regions. The oceanic regions exhibit broad summer maximum and the maximum to minimum ratio is the lowest for these regions. The concentration of tropospheric column ozone is found to be highest in North Eastern India (NE) and the Indo Gangetic plains (IGP). NE ozone concentration exceeds that of IGP during spring whereas in post monsoon and winter reverse is the case. In the monsoon season, O3 levels in the two regions are equal. The spring time highest level of tropospheric column ozone over NE region is found to be associated with highest incidence of lightning and biomass burning activity. The Stratosphere-Troposphere exchange is also found to contribute to the enhanced level of ozone in spring in NE India. A net decrease in tropospheric ozone concentration over NE during the period 1979 to 2009 has been observed

Prevalence and correlates of arthritis in Indian older adults: Findings from the longitudinal aging study of India

There are no estimates of arthritis in older adults from a nationally representative population in India and this is a major gap in evidence for effective policy making. Therefore, this study was planned to use secondary data available from the Longitudinal Aging Study of India (LASI) to estimate the prevalence of arthritis and study its risk factors in older adults from India. We used data from the first wave of LASI, a national and state-level study of aging and health in India. Weighted prevalence with 95% confidence intervals (CI) for Arthritis was estimated in different age groups. We built unadjusted and adjusted logistic regression models for identifying the risk factors associated with arthritis. We also evaluated the relationship between functional dependence and arthritis using a multivariable regression model. Overall, 9.36% of the Indian population aged 45 years or above had Arthritis. The prevalence was 7.49% and 11.03% in males and females respectively. Females are more at risk for arthritis as compared to males with an odds ratio of 1.59 (95% CI: 1.50, 1.69).  Age also was a significant risk factor with an adjusted odd ratio of 1.41 (95% CI: 1.31, 1.52)

An Investigation of Aerosol Size Distribution Properties at Dibrugarh: North-Eastern India

Columnaraerosol size distributions, retrieved from spectral AOD (aerosol optical depth) esti mates over a northeastern location of India (Dibrugarh) are, ingeneral, bimodal with the occurrence of primary (broad) mode at 0.04 - 0.17 m and the secondary mode at 0.88 - 1.29 m. The physical parameters of size distributions representing the microphysical properties of aerosols show distinct sea sonal variations with the highest value of the effec tiveradii (~ 0.55 m) during pre-mon soon (March to May) season which, along with the highest value of AOD (~ 0.46 ¡__n0.09) during the same season, is attributed to the maxi mum abundance of coarsepa ticles. Examining the re sults in the light of the HYSPLIT back tra jectory analy sis and the peruliar to pogr phy of northeast India allowing advection only from the Indo-Gangetic plains or Bay-of-Ben gal, it appears that the strong presence of the coarse mode aerosols are associated with either mineral dust or marine aerosol components or both

Short term introduction of pollutants into the atmosphere at a location in the Brahmaputra Basin: A case study

AbstractIntensive fire ignition and cracker work activities takes place during the festival of light called Diwali in India, celebrated for a period of few days in the month of October or November every year. The firecracker releases several pollutants [such as particulate matter (PM), black carbon (BC), organics, trace gases] near the surface. The effect of firecrackers on the atmospheric constituents is evaluated over Dibrugarh by monitoring the concentrations of PM, PM10 (particle radius ≤10 µm), PM2.5 (particle radius ≤2.5 µm) and BC during the Diwali and post-Diwali days (5 days after the Diwali Festival) in the years 2009 and 2010. Monthly average concentrations of each species except for the Diwali and post Diwali days is considered as the background concentrations. The concentration levels of the pollutants as recorded on the Diwali days are found to be a number of times higher (5.33 and 2.50 times for PM10, 5.74 and 2.65 times for PM2.5, 1.21 and 1.66 times for BC for the year 2009 and 2010, respectively) than the background levels at the peak hours of the fire work activity. To delineate the contribution of fireworks to the high concentrations of the species we performed air mass back trajectory analysis using the NOAA–HYSPLIT model in order to examine the existence of the transported aerosols. The ten day accumulated MODIS fire maps are also analyzed to mark out the contribution of aerosols from biomass burning. These analyses reveal that the higher concentrations of near surface aerosols including BC during the festival is due to the local effect of firework activities, neither because of long–range transport nor due to biomass burning activities. However, the higher concentration of pollutants for short periods has not degraded air quality substantially to cause health risks to people exposed to the festival in this environment

Multi-Model Evaluation of Meteorological Drivers, Air Pollutants and Quantification of Emission Sources over the Upper Brahmaputra Basin

The temporal distributions of meteorological drivers and air pollutants over Dibrugarh, a location in the upper Brahmaputra basin, are studied using observations, models and reanalysis data. The study aims to assess the performance of the Weather Research and Forecasting model coupled with chemistry (WRF-Chem), the WRF coupled with Sulfur Transport dEposition Model (WRF-STEM), and Copernicus Atmosphere Monitoring Service (CAMS) model over Dibrugarh for the first time. The meteorological variables and air pollutants viz., black carbon(BC), carbon monoxide(CO), sulphur dioxide(SO2), Ozone(O3), and oxides of Nitrogen(NOx) obtained from WRF-Chem, WRF-STEM and CAMS are evaluated with observations. The source region tagged CO simulated by WRF-STEM delineate the regional contribution of CO. The principal source region of anthropogenic CO over Dibrugarh is North-Eastern India with a 59% contribution followed by that from China (17%), Indo-Gangetic Plains (14%), Bangladesh (6%), other parts of India (3%) and other regions (1%). Further, the BC-CO regression analysis is used to delineate the local emission sources. The BC-CO correlations estimated from models (0.99 for WRF-Chem, 0.96 for WRF-STEM, 0.89 for CAMS), and reanalysis (0.8 for Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA2) are maximum in pre-monsoon whereas surface observations show highest correlations (0.81) in winter. In pre-monsoon season, 90% of the modeled CO is due to biomass burning over Dibrugarh

Loss of crop yields in India due to surface ozone: an estimation based on a network of observations

Surface ozone is mainly produced by photochemical reactions involving various anthropogenic pollutants, whose emissions are increasing rapidly in India due to fast-growing anthropogenic activities. This study estimates the losses of wheat and rice crop yields using surface ozone observations from a group of 17 sites, for the first time, covering different parts of India. We used the mean ozone for 7 h during the day (M7) and accumulated ozone over a threshold of 40 ppbv (AOT40) metrics for the calculation of crop losses for the northern, eastern, western and southern regions of India. Our estimates show the highest annual loss of wheat (about 9 million ton) in the northern India, one of the most polluted regions in India, and that of rice (about 2.6 million ton) in the eastern region. The total all India annual loss of 4.0-14.2 million ton (4.2-15.0%) for wheat and 0.3-6.7 million ton (0.3-6.3%) for rice are estimated. The results show lower crop loss for rice than that of wheat mainly due to lower surface ozone levels during the cropping season after the Indian summer monsoon. These estimates based on a network of observation sites show lower losses than earlier estimates based on limited observations and much lower losses compared to global model estimates. However, these losses are slightly higher compared to a regional model estimate. Further, the results show large differences in the loss rates of both the two crops using the M7 and AOT40 metrics. This study also confirms that AOT40 cannot be fit with a linear relation over the Indian region and suggests for the need of new metrics that are based on factors suitable for this region