Standard Classification Scheme Research Articles

FACIES PARTITIONING AT REGIONAL AND FIELD SCALES IN THE BARREMIAN KHARAIB‐2 CARBONATES, UAE

Carbonates in the Lower Cretaceous (Barremian to early Aptian) Kharaib Formation are reservoir rocks at giant oil fields in the UAE and Qatar. The Barremian Kharaib‐2 member (K60), the focus of this study, is in general composed of a regionally continuous succession of high‐energy, shallow‐water limestones bounded above and below by “dense” low‐energy mud‐rich strata. Despite several decades of research, conventional carbonate facies classification schemes and resulting facies groupings for the Kharaib‐2 member have failed to show a statistically acceptable correlation with core‐ and log‐derived petrophysical data. Moreover, sedimentary bodies potentially responsible for dynamic reservoir heterogeneities have not clearly been identified. This paper proposes a standardized facies classification scheme for the Kharaib‐2 carbonates based on vertical facies proportion curves (VPCs) and variogram analyses of core data to construct stratigraphic correlations at both field and regional scales. Data came from 295 cored wells penetrating the Kharaib‐2 member at ten fields in the on‐ and offshore UAE. Thin, dense intervals separating reservoir units were adopted as fourth‐order transgressive units and were used for stratigraphic correlation. Field‐scale probability maps were used to identify sedimentary bodies such as shallow‐water rudistid shoals.Regional stratigraphic correlations of the Kharaib‐2 member carbonates based on the VPCs identified variations in depositional environments, especially for the lower part of the reservoir unit; depositional facies at fields in the SE of the UAE were interpreted to be more distal compared to those at offshore fields to the NW. At a field scale, the VPCs failed to identify significant lateral variations in the carbonates. However, variogram analyses of cored wells showed spatial concentrations of specific facies in the inner ramp domain which could be correlated with high‐energy depositional bodies such as shoals dominated by rudist debris. The bodies were sinusoidal in plan view with lengths of up to 8 km and widths of ca. 1 km. Although similar‐shaped bodies with these dimensions have been reported from other carbonate depositional systems, they have not previously been reported in the Kharaib Formation. At a regional (inter‐field) scale, the stratigraphic correlation of standardized sedimentary facies remains problematic; however, mapping of facies associations and their relative proportions relative to their environments of deposition demonstrated new patterns for the stratigraphic architecture of the Kharaib‐2 member in the UAE.

Differences in LCZ composition according to urban planning and impacts on urban thermal environment

As a climate-aware and standardized classification scheme, the Local Climate Zone (LCZ) can be a research framework for analysis of effects of urban development on urban forms and Urban Heat Island (UHI) phenomenon in relation to urban planning. In this study, two new towns built with different urban planning in South Korea are selected as the study areas. In respect of the growing popularity of deep learning and advancements deep learning-based LCZ classification, we build a deep convolutional neural network architecture using a modified SE-ResNext50 backbone. As input data, we designed six schemes using different combinations of Sentinel-1 SAR and Sentinel-2 MSI imagery and thermal band from Landsat 9 is used for SUHI magnitude estimation and heat vulnerability. In addition, robust and quantitative data sampling using building surface fraction data and building height data was performed. The results indicate that combining SAR and multispectral data could increase LCZ classification accuracy and outline the capacity of polarimetric decomposition components. In addition, it was suggested that urban planning causes differences in the LCZ distribution of each new town, resulting in differences in SUHI magnitude and heat vulnerability. The research findings can help guide future urban development by considering the urban form and thermal environment according to the LCZ composition within the new plannedcity.

The geomorphology of Ireland’s continental shelf

ABSTRACT The Irish Shelf Seabed Geomorphological Map (ISSGM) (v2023) presented here, is the first high-resolution geomorphological map of the entire Irish continental shelf. This large-scale mapping exercise took advantage of the vast INFOMAR multibeam echosounder dataset, and used a protocol of semi-automated mapping techniques to accurately and rapidly extract seabed features. All previous mapping efforts and existing literature on the Irish shallow shelf geomorphology have also been collated and integrated in the map, critically evaluating the previous interpretations. An internationally standardised classification scheme was adopted, aligning the ISSGM (v2023) to other international geomorphological work. At a national level, this detailed geomorphological digital map is intended primarily as a resource to better inform multiple offshore activities and management of the marine environment. The map also acts as a baseline for future studies in marine geomorphology, as it identifies gaps in the knowledge and highlights areas of contentious interpretation that require further work. The map is available online on the Irish Marine Atlas (https://atlas.marine.ie – Geology Theme).

A catalogue of cataclysmic variables from 20 yr of the Sloan Digital Sky Survey with new classifications, periods, trends, and oddities

ABSTRACT We present a catalogue of 507 cataclysmic variables (CVs) observed in SDSS I to IV including 70 new classifications collated from multiple archival data sets. This represents the largest sample of CVs with high-quality and homogeneous optical spectroscopy. We have used this sample to derive unbiased space densities and period distributions for the major sub-types of CVs. We also report on some peculiar CVs, period bouncers and also CVs exhibiting large changes in accretion rates. We report 70 new CVs, 59 new periods, 178 unpublished spectra, and 262 new or updated classifications. From the SDSS spectroscopy, we also identified 18 systems incorrectly identified as CVs in the literature. We discuss the observed properties of 13 peculiar CVS, and we identify a small set of eight CVs that defy the standard classification scheme. We use this sample to investigate the distribution of different CV sub-types, and we estimate their individual space densities, as well as that of the entire CV population. The SDSS I to IV sample includes 14 period bounce CVs or candidates. We discuss the variability of CVs across the Hertzsprung–Russell diagram, highlighting selection biases of variability-based CV detection. Finally, we searched for, and found eight tertiary companions to the SDSS CVs. We anticipate that this catalogue and the extensive material included in the Supplementary Data will be useful for a range of observational population studies of CVs.

Statistical Analysis of Wood Durability Data and Its Effect on a Standardised Classification Scheme

The biological durability of wood is an important property for outdoor applications of wood-based products. In temperate climate zones, the most critical biological hazard is wood-destroying fungi, and the European standard EN 350 in combination with EN 113-2 provide guidance on sampling, testing, and classifying wood durability against brown and white rot fungi. However, in their latest revised versions, both standards recommend the use of probability density functions for fitting mass loss data (ML). Subsequently, the durability of wood and its variability should be further characterised. The aim of this study was to statistically analyse the ML data from laboratory agar plate tests with different European-grown wood species and to examine the effect of different statistical treatments on the standardised classification scheme of wood durability. It was concluded that more precise guidance is needed on the sampling procedure since significant differences in durability exist between stem zones. The assignment of dispersion indicators requires a revision to ensure clear, unmistakable, and reproducible durability classification of wood. Deficits in the description of the proposed statistical treatments in both standards became evident. It can be questioned whether the application of probability density functions provides additional information about the variability of wood durability.

The status quo of Indian data repositories indexed in re3data registry

PurposeThe purpose of this study was to understand the landscape of Indian research data repositories (RDRs) indexed in the re3data.org. The study analysed the metadata elements of Indian RDRs to identify their disciplinary orientations, typology, standards adopted, foreign collaborations, etc. The study ascertained the current status of the Indian RDRs by visiting their respective websites and tried to identify and map the exact disciplinary orientation of each RDR.Design/methodology/approachThe study used “content analysis” of the metadata elements extracted from re3data.org along with the information analysis of the respective websites of the registered RDRs.FindingsThe study identified that only 80% of the Indian RDRs listed by the re3data.org is currently active. Most of the Indian RDRs are hosted by the central and state governments and are almost equally distributed among Life Sciences, Natural Sciences and Social Sciences domains. The data provided by the re3data.org for the Indian RDRs are not complete and up-to-date.Practical implicationsThe findings indicate the presence of a good number of inactive RDRs in the re3data.org. The study suggests using a revised version of the DFG subject classification scheme or considering a standard classification scheme for subject indexing.Originality/valueTo the best of the authors’ knowledge, this study is the first of its kind that critically analysed the metadata values extracted and moved further to identify the current status of Indian RDRs.

Standardized classification schemes in reporting oncologic PET/CT.

The imaging report is essential for the communication between physicians in patient care. The information it contains must be clear, concise with evidence-based conclusions and sufficient to support clinical decision-making. In recent years, several classification schemes and/or reporting guidelines for PET have been introduced. In this manuscript, we will review the classifications most frequently used in oncology for interpreting and reporting 18F-FDG PET imaging in lymphoma, multiple myeloma, melanoma and head and neck cancers, PSMA-ligand PET imaging for prostate cancer, and 68Ga-DOTA-peptide PET in neuroendocrine tumors (NET).

Hydrological Drought Generation Processes and Severity Are Changing in the Alps

AbstractStreamflow droughts are governed by different hydro‐meteorological processes, whose relative importance may change over time, with potential impacts on drought severity. Here, we assess changes in the importance of different hydrological drought generation processes in the European Alps by applying a standardized drought type classification scheme to two time periods—one in the distant (1970–1993) and one in the recent past (1994–2017). Our findings show that changes in the relative importance of different drought generation processes are stronger in high‐elevation catchments, where we detect clear changes in drought seasonality, than in low‐elevation catchments. Furthermore, they suggest that changes in drought severity and generation processes are related because increasingly frequent snowmelt‐deficit droughts in high‐elevation catchments have larger deficits than droughts caused by decreasingly frequent cold temperatures. These changes might persist into the future, because of continuing decreases in snow cover and increases in evapotranspiration, with potential implications for water management.

The characteristic and transformation of 3D urban morphology in three Chinese mega-cities

Urban morphology exerts an important influence on human settlements and serves to promote sustainable development. However, few studies have focused on the three dimensions (3D) of urban morphology. Therefore, to explore 3D urban morphological characteristics and transformations, we adopted a standard urban morphology classification scheme, the local climate zone (LCZ), and selected three Chinese megacities as case studies. Remote sensing images and machine-learning technology were used to create spatiotemporal LCZ maps. Our results reveal that cities have diverse forms, with the dominant urban morphology varying across different cities and periods. However, cities still share several common patterns. The dynamic spatial distribution of LCZ density can be divided into three patterns: spreading, shrinking and relocation. In recent years, Chinese megacities have mainly expanded with open high-rise (OHR) and large low-rise (LLR) classes. Combined with the increase in building height, urban landscapes have been homogeneous. Additionally, 3D urban morphology is driven by several factors, such as economic activity and the dissociation of population growth from urban expansion. Detailed spatiotemporal LCZ maps can help us better understand urban morphology transformations and explore the corresponding driving forces, providing a reference for urban planners and policymakers seeking to make cities healthier and more livable.

Prediction of Health Care Data Using Efficient Machine Learning Algorithms

Every clinical decision relies on the doctor's expertise and comprehension.This standard procedure may, despite appearances, lead to errors, biases, and increased costs that compromise the patients' Quality of Service (QoS).There is a pressing need for adaptable equipment for critical patient care in developing nations like India.The majority of Indian hospitals are unable to provide their patients with adequate medical care due to a lack of suitable, simple, and expandable intelligent systems.The development of a comprehensive system that will enable hospitals to provide vital patients with a real-time feedback system is the objective of this project.Using IBM cloud computing as a service platform and machine learning, we propose a standard architecture, language, and classification scheme for analyzing vital patient health data (PaaS).The development of a machine learning (ML) method for predicting a patient's fitness is the primary goal of this study.Our models and data are stored and managed by IBM Watson Studio and IBM Cloud.The Base Predictors for our ml models are Nave Bayes, Logistic Regression, the KNeighbors Classifier, the Decision Tree Classifier, the Random Forest Classifier, the Gradient Boosting Classifier, and the MLP Classifier.The precision of the model has been increased by employing the ensemble learning bagging strategy.We use a variety of machine learning algorithms for ensemble learning.The Critical Patient Management System, or CPMS, is a mobile application we developed that allows for real-time data and record viewing.Data that is relevant to ML model training and deployment can be fetched in real time from IBM Cloud and made available through CPMS because of the way the system is built.Doctors can use Ml tendencies to predict a patient's health status.The CPMS will send an SMS notification to the duty physician and nurse to provide immediate care if the situation worsens as anticipated.Hospitals might get a smart healthcare solution if the mission, milliliter models, and mobile application are combined.

Coupling Dual Graph Convolution Network and Residual Network for Local Climate Zone Mapping

Local climate zone (LCZ) has become a new standard classification scheme in urban landscapes and showed great potential in urban climate research. Traditional classifiers and ordinary neural networks only consider the spectral or local spatial features of the pixel, ignoring the effect of nonlocal information on the LCZ classification. The graph convolutional network (GCN) has been used to exploit the relationship between adjacent and global land covers owing to the ability to conduct flexible convolution over graphs. In this work, we integrated a convolutional neural network and two GCNs into an end-to-end hybrid framework and generated LCZs directly from the original images. Local-, regional-, and global-level features were extracted and grouped complementarily to foster better performance. Experiments were conducted in six cities around the world to verify the effectiveness of our method. Results showed that the average classification accuracy of the six cities reached 0.956 and performed better than any other comparable model. Ablation experiments also demonstrated the mutual promotion of the different modules. Finally, the small sample experiment provided a practical reference for the LCZ classification in the absence of samples in future.

Identifying Black Corals and Octocorals From Deep-Sea Imagery for Ecological Assessments: Trade-Offs Between Morphology and Taxonomy

An increased reliance on imagery as the source of biodiversity data from the deep sea has stimulated many recent advances in image annotation and data management. The form of image-derived data is determined by the way faunal units are classified and should align with the needs of the ecological study to which it is applied. Some applications may require only low-resolution biodiversity data, which is easier and cheaper to generate, whereas others will require well-resolved biodiversity measures, which require a larger investment in annotation methods. We assessed these trade-offs using a dataset of 5 939 images and physical collections of black and octocorals taken during surveys from a seamount area in the southwest Pacific Ocean. Coral diversity was greatly underestimated in images: only 55 black and octocoral ‘phototaxa’ (best-possible identifications) were consistently distinguishable out of a known 210 species in the region (26%). Patterns of assemblage composition were compared between the phototaxa and a standardized Australian classification scheme (“CATAMI”) that uses morphotypes to classify taxa. Results were similar in many respects, but the identities of dominant, and detection of rare but locally abundant, coral entities were achieved only when annotation was at phototaxon resolution, and when faunal densities were recorded. A case study of data from 4 seamounts compared three additional classification schemes. Only the two with highest resolution – phototaxon and a combined phototaxon-morphological scheme – were able to distinguish black and octocoral communities on unimpacted vs. impacted seamounts. We conclude that image annotation schemes need to be fit-for-purpose. Morphological schemes such as CATAMI may perform well and are most easily standardized for cross-study data sharing, but high resolution (and more costly) annotation schemes are likely necessary for some ecological and management-based applications including biodiversity inventory, change detection (monitoring) – and to develop automated annotation using machine learning.

Ultrasonographic prevalence and proposed morphologic classification of bilobed gallbladder in cats.

Morphologic anomalies of the feline gallbladder (GB) have been previously reported in the literature. These morphologic variants are frequently encountered on routine abdominal ultrasound examination. The aim of this study was to provide an ultrasonographic classification system of these variants and document the overall incidence in the feline population. A prospective, descriptive study was undertaken; cats that had an abdominal ultrasound examination that included at least one sagittal and transverse plane image of the GB were included. GB shape was evaluated and categorized based on a classification scheme of morphologic variants modified from the human literature. Septated (S), bilobed (B1, B2, B3), duplex (D) and complex (C) categories were described. Of 516 cats included in the study, 389 had normal GB morphology, while 127 had anomalous GB morphology. The overall incidence rate of anomalous GB morphology was 24.61%. When examined by morphologic type, the septated (S) morphology had an incidence of 9.69%. A bilobed (B) morphology was the most commonly observed classification; incidence was 14.35% within our population; incidence of B1, B2 and B3 subtypes were 2.91%, 6.98% and 4.46%, respectively. Duplex GBs only made up 0.39% of the total population. The incidence of complex (C) morphologies was 0.19%. The incidence of GB morphologic anomalies was higher in our population than previously reported. Identification of these anomalies on routine ultrasound evaluation is common; numerous different morphologies can be identified and a standardized classification scheme is proposed. Complete evaluation of morphology can be challenging, particularly with regard to cystic duct anatomy. Clinical significance is uncertain and future studies are warranted to determine the relationship between morphologic variants and hepatobiliary disease.

Local climate zone classification using a multi-scale, multi-level attention network

Local Climate Zones (LCZ) offer a climate-aware and standardized classification scheme composed of 17 urban and natural landscape classes. Recent deep learning-based LCZ classification studies have adopted a scene classification approach with computer vision-inspired models. In light of these advancements, this study introduces a multi-scale, multi-level attention network (MSMLA-Net) for deep learning-based LCZ classification. MSMLA-Net integrates a multi-scale (MS) module to generate multi-scale features from the input data and a novel multi-level attention (MLA) module as a branch unit from the model’s main ResNet backbone. MLA uses the convolutional block attention module (CBAM) at multiple stages to generate multi-level spatially and spectrally enhanced features for context aggregation. This study presents comprehensive model-based experiments on model depth, the individual and combined influence of MS and MLA modules, and the addition of attention mechanisms. Furthermore, data-based experiments are conducted to determine optimal Sentinel-2 spectral bands, while OpenStreetMap (OSM) building data, ALOS World 3D DSM height information, and a national land cover map are included as ancillary bands. LCZ classification tests are conducted on six major cities in South Korea. With regards to model-based performance, MSMLA-Net was built using a modified SE-ResNet50 backbone (MSMLA-50) and obtained the best classification results using 48 by 48-pixel input patches with a combination of all Sentinel-2 and ancillary bands, outperforming three state-of-the-art LCZ classification models. In particular, only MSMLA-50 reached over 70% built-up overall accuracy and maintained high accuracy even when tested on completely unseen areas in a “citywise” leave-one-out sampling strategy. For data-based results, the combination of Sentinel-2 Red Edge bands were helpful, mainly due to the greater number of available bands. Training only ancillary data generated up to 75.0% for built-up overall accuracy, albeit at the cost of low natural overall accuracy. Using all available bands and ancillary data produced the best results, but a combination of only OSM and Sentinel-2 bands also generated comparable accuracy. Ultimately, the proposed MSMLA-Net bridges advanced computer vision techniques such as attention mechanisms and multi-level context aggregation to improve LCZ classification. The MS and MLA modules can be applied on different backbones, sophisticated sampling schemes, and data inputs to flexibly achieve improved classification results.

The Fable of the Beetle in Contemporary Aramaic and Kurmanji

Abstract The fable of an insect and a mouse (or some other animal), who marry and embark on a life together, only to end in tragedy, is widely disseminated from the Mediterranean region to India. One version involving a beetle (Ṭuroyo keze, Kurmanji kêz) circulates throughout Anatolia and Iraq. The following Ṭuroyo and Kurmanji version was recorded during the 2020 summer field season of the Russian expedition to Ṭur Abdin in the village of Dērqube from a speaker of the Bequsyone dialect. She relates the narrative portions of the fable in Ṭuroyo, but switches to Kurmanji for its versified portions. In addition to the text and a translation, this study includes an interlinear glossing. It also discusses the motifs of the fable according to the standard classification scheme, as well as its relationship to other attested versions collected in various languages including Arabic, Kurmanji, and Turkish.

The Key Reason of False Positive Misclassification for Accurate Large-Area Mangrove Classifications

Accurate large-area mangrove classification is a challenging task due to the complexity of mangroves, such as abundant species within the mangrove category, and various appearances resulting from a large latitudinal span and varied habitats. Existing studies have improved mangrove classifications by introducing time series images, constructing new indices sensitive to mangroves, and correcting classifications by empirical constraints and visual inspections. However, false positive misclassifications are still prevalent in current classification results before corrections, and the key reason for false positive misclassification in large-area mangrove classifications is unknown. To address this knowledge gap, a hypothesis that an inadequate classification scheme (i.e., the choice of categories) is the key reason for such false positive misclassification is proposed in this paper. To validate this hypothesis, new categories considering non-mangrove vegetation near water (i.e., within one pixel from water bodies) were introduced, which is inclined to be misclassified as mangroves, into a normally-used standard classification scheme, so as to form a new scheme. In controlled conditions, two experiments were conducted. The first experiment using the same total features to derive direct mangrove classification results in China for the year 2018 on the Google Earth Engine with the standard scheme and the new scheme respectively. The second experiment used the optimal features to balance the probability of a selected feature to be effective for the scheme. A comparison shows that the inclusion of the new categories reduced the false positive pixels with a rate of 71.3% in the first experiment, and a rate of 66.3% in the second experiment. Local characteristics of false positive pixels within 1 × 1 km cells, and direct classification results in two selected subset areas were also analyzed for quantitative and qualitative validation. All the validation results from the two experiments support the finding that the hypothesis is true. The validated hypothesis can be easily applied to other studies to alleviate the prevalence of false positive misclassifications.

A standardised classification scheme for the Mid-Holocene Toalean artefacts of South Sulawesi, Indonesia.

The archaeology of Sulawesi is important for developing an understanding of human dispersal and occupation of central Island Southeast Asia. Through over a century of archaeological work, multiple human populations in the southwestern region of Sulawesi have been identified, the most well-documented being that of the Mid- to Late Holocene ‘Toalean’ technological period. Archaeological models for this period describe a population with a strong cultural identity, subdivided into groups living on the coastal plains around Maros as well as dispersed upland forest dwellers, hunting endemic wildlife with bow-and-arrow technology. It has been proposed that the Toaleans were capable of vast water-crossings, with possible cultural exchange with northern Australia, Java, and Japan. This Toalean paradigm is built almost exclusively on existing interpretations of distinctive Toalean stone and bone artefact technologies, constructed on out-dated 19th and 20th century theory. Moreover, current definitions of Toalean artefact types are inconsistently applied and unsystematic, and the manufacturing sequence has historically been poorly understood. To address these problems in existing artefact models and typologies, we present a clarified typology of the Toalean artefacts of South Sulawesi, and describe the technical aspects of artefact production. This typology provides a tool for standardising research and will facilitate more meaningful assessments of material culture repertoires and more reliable assessment of spatial and temporal changes for the region.

Discrimination of SINTOX® GSR against environmental particles and its automated investigation by SEM/EDS

A fundamental problem in gunshot residue (GSR) analysis by SEM/EDS is the distinction to environmental particles. The introduction of heavy metal free (HMF) primers such as SINTOX® enhanced this problem. According to the current ASTM E1588 Standard Practice, the identification of SINTOX® GSR by means of an automated classification scheme is based on the simultaneous presence of titanium and zinc in a particle, labelling them just as “consistent with GSR”. Since both elements often appear together in environmental particles, this leads to a certain number of false-positive findings. Therefore a large number of environmental particles originating from different sources such as waste incineration plants and volcanic ashes were analytically evaluated. In order to find possible distinctions between SINTOX® primer GSR and environmental particles, the components of SINTOX® ammunition and its generated residues resulting from the firing process were examined.A new classification scheme of high efficiency for SINTOX® GSR was developed extending the standard classification scheme based on the recommendations of the ASTM E1588 Standard Practice. The resulting new classes show a good selectivity and nearly no overlap with environmental particles. In casework, the new proposed classification process can reduce the total investigation time due to an improvement in the manual confirmation step of SINTOX® GSR.

Breakpoints for the Classification of Anti-Candida Compounds in Antifungal Screening.

Introduction The absence of a standardized classification scheme for the antifungal potency of compounds screened against Candida species may hinder the study of new drugs. This systematic review proposes a scheme of interpretative breakpoints for the minimum inhibitory concentration (MIC) of bioactive compounds against Candida species in in vitro tests. Materials and Methods A literature search was conducted in the PubMed, Scopus, Web of Science, Lilacs, and SciFinder databases for the period from January 2015 to April 2020. The following inclusion criterion was used: organic compounds tested by the microdilution technique according to the Clinical and Laboratory Standards Institute protocol against reference strains of the genus Candida. A total of 545 articles were retrieved after removing duplicates. Of these, 106 articles were selected after applying the exclusion criteria and were evaluated according to the number of synthesized molecules and their chemical classes, the type of strain (reference or clinical) used in the antifungal test, the Candida species, and the MIC (in μg/mL) used. Results The analysis was performed based on the median, quartiles (25% and 75%), maximum, and minimum values of four groups: all strains, ATCC strains, C. albicans strains, and C. albicans ATCC strains. The following breakpoints were proposed to define the categories: MIC < 3.515 μg/mL (very strong bioactivity); 3.516-25 μg/mL (strong bioactivity); 26-100 μg/mL (moderate bioactivity); 101-500 μg/mL (weak bioactivity); 500-2000 μg/mL (very weak bioactivity); and >2000 μg/mL (no bioactivity). Conclusions A classification scheme of the antifungal potency of compounds against Candida species is proposed that can be used to identify the antifungal potential of new drug candidates.

Pneumosinus Dilatans: A Novel Algorithm for its Classification and Management.

Pneumosinus dilatans (PD) is a rare condition in which one or more paranasal sinuses are pathologically hyperaerated. Patients commonly present with an aesthetic complaint, however it has been associated with several serious conditions. The etiology is largely theoretical and the management remains unstandardized, potentially leading to missed diagnoses. This article aims to establish a presumptive classification scheme and management algorithm based on a comprehensive review of the literature. A systematic review of all available publications on the search term "Pneumosinus dilatans" was conducted in PubMed. Associated conditions were identified and clustered based on the number and anatomic location of the sinuses involved. The resultant data was used to formulate an evidence-based algorithm for the evaluation and management of PD patients. A total of 145 patients from 103 articles were included. The majority of patients were male (57.2%) with an average age of 32.6 years-old at onset. The frontal sinus was most commonly involved (62.8%) and one pathologic sinus was most common (84.6%). 51% of patients presented with an associated condition; with it least common in frontal sinus PD (29.7% cases) and most common in ethmoid sinus PD (81.5%). To date, no articles to date have proposed a management algorithm for PD based on the number or location of sinus involved. A standardized evidence-based management algorithm and classification scheme for patients with PD will not only lead to the highest-quality treatment, but will also be instrumental in elucidating an etiology for this rare, and potentially serious, condition.