Expert Tool Research Articles

KoopaML, a Machine Learning platform for medical data analysis

Machine Learning allows facing complex tasks related to data analysis with big datasets. This Artificial Intelligence branch allows not technical contexts to get benefits related to data processing and analysis. In particular, in medicine, medical professionals are increasingly interested in Machine Learning to identify patterns in clinical cases and make predictions regarding health issues. However, many do not have the necessary programming or technological skills to perform these tasks. Many different tools focus on developing Machine Learning pipelines, from libraries for developers and data scientists to visual tools for experts or platforms to learn. However, we have identified some requirements in the medical context that raise the need to create a customized platform adapted to end-user found in this context. This work describes the design process and the first version of KoopaML, an ML platform to bridge the data science gaps of physicians while automatizing Machine Learning pipelines. The platform is focused on enhanced interactivity to improve the engagement of physicians while still providing all the benefits derived from the introduction of Machine Learning pipelines in medical departments, as well as integrated ongoing training during the use of the tool’s features.

Development and validation of the Turkish version of the Mobile App Rating Scale – MARS-TR

BackgroundThe number of mobile health apps (MHAs) is growing rapidly. MHAs have great potential to improve health and health care. However, the quality of available MHAs remains unknown due to the lack of quality assessment regulations and standards for MHAs. The Mobile Application Rating Scale (MARS) is the most widely used instrument to assess the quality of MHAs, and available in English, Italian, Spanish, German, French, Arabic and Japanese. However, the scale is currently not available in the Turkish language. ObjectiveThis study aimed to cross-culturally adapt the MARS into Turkish and evaluate the validity and reliability of the scale. MethodsThe MARS was translated and adapted into Turkish according to the international guidelines. A total of 52 pregnancy tracking apps were evaluated by two independent raters. Internal consistency (Cronbach’s alpha), inter-rater reliability (Intraclass Correlation Coefficient [ICC]), convergent validity and concurrent validity were explored. Regarding convergent validity, MARS-TR scores were compared with the ENLIGHT scale. ResultsThe MARS-TR was highly aligned with the original MARS. The MARS-TR showed excellent internal consistency (Cronbach’s alpha of 0.95 for both raters) and excellent inter-rater reliability (ICC = 0.94; SEM = 0.02), with a smallest detectable change (95 % confidence level) of 0.05 points for the total score. Cronbach’s alphas for the subscales ranged from 0.76 to 0.94 for the two raters. Correlations between the MARS-TR and ENLIGHT demonstrated adequate convergent validity (P < 0.05). No ceiling or floor effects were observed. ConclusionThe results provide evidence that the Turkish version of MARS is a valid and reliable tool for researchers and experts to assess the quality of MHAs in Turkey.

Application of the IRAP Method Combined with GIS to Improve Road Safety on New Highway Projects in Algeria

This article aims to proactively predict high-risk accident areas in a new highway project in terms of its technical and geometric characteristics. The purpose of this study is to provide road project managers with better road safety assessment tools for the vehicle occupant category. The methodology used combines the International Road Assessment Program (IRAP) methodology and the Geographic Information System (GIS). The IRAP program enables the calculation and classification of risks for each 100-meter segment besides their categorization using a star rating; whereas, the GIS is used to map the risks and identify high-risk accident segments. This method was applied to a new highway project of 110 hectometres located in GHAZAOUET in Algeria. The results of the study revealed that this combined method is innovative in more than one way as it is an effective decision-making aid tool for road safety experts. It, therefore, makes it possible to target accident-prone sections in a faster way before carrying out on site road safety inspections. Technically speaking, this study shows that the existence of closely curved radii mixed at traffic speeds exceeding 80 km/h and the presence of road exits are the main factors affecting the safety of vehicle occupants. Speed control; thus, remains one among the cheapest measures to improve safety.

FORENSIC IDENTIFICATION BY LIP PRINTS. HISTORY, METHODS, THE QUESTION THAT ARE SOLVED

For almost a century, methods of identifying a person by prints have been recognized as a reliable tool for forensic experts in the world. Cheiloscopic examination is used to solve a wide range of issues facing the investigation, especially in cases of absence of other material evidence. The article reveals the historical stages of development and improvement of methods of examination of lip prints, a list of issues that can be solved using such methods. Materials of the practical achievements of world scientists in the application of cheiloscopic research methods are provided.

Formwork System Selection in Building Construction Projects Using an Integrated Rough AHP-EDAS Approach: A Case Study

The successful completion of reinforced concrete (RC) building construction projects depends, in part, on selecting the appropriate formwork system (FWS) since it may significantly affect the project’s cost, time, and quality performance factors. The selection of the FWS depends on a number of compromising and conflicting criteria, while several FWS alternatives may be available. Therefore, the FWS selection has mostly been treated as a multi-criteria-decision-making (MCDM) problem. Although various MCDM methods have been employed to address the FWS selection problem, none have considered the subjectivity and uncertainty arising from a group decision-making process. This study aims to fill this knowledge gap by proposing an integrated approach using recently developed MCDM methods with rough numbers. In the integrated approach, first, a decision-making team is formed to develop the decision hierarchy. Then, the rough analytic hierarchy process (R-AHP) is used to determine rough criteria weights, followed by the rough evaluation based on the distance from average solution (R-EDAS) method to rank the FWS alternatives. Finally, the results are compared using different rough MCDM methods to ensure the stability of the proposed approach. The proposed approach is applied to a real-life building construction project in Turkey to select the most appropriate FWS. The integrated approach was found to be effective, and it was recommended to be used for future FWS selection problems. The proposed integrated approach in this study may be used as a decision support tool for construction professionals and experts to select the FWS in building construction projects.

Automated search of process control limits for fault detection in time series data

Manually defined control limits remain a common strategy for quality control in manufacturing due to their ease of deployment on the shop floor compared to more advanced data analysis approaches. Despite their continued importance, there is no systematic method of defining these control limits. However, sub-optimal control limits can lead to undetected faults or cause unnecessary interruption to production. This manuscript presents an algorithm that systematizes this manual process into an efficient search task. We conceptualized the search task as a sequence of sub-problems that are based on the conventional steps taken by process experts when defining control limits. This algorithm can be integrated into an expert tool for shop floor personnel to automate the definition of control limits in annotated time series data. We demonstrate the efficacy of the control limits found by our algorithm by comparing them to those manually defined by process experts in real-world process data from the automotive industry. Furthermore, we show that our algorithm generalizes to traditional time series classification problems and achieves state-of-the-art performance on selected benchmark datasets. Our work is the first effort in automating the otherwise manual definition of control limits for fault detection.

Mapping the Transformation Potential of Streets Using Urban Planning Parameters and Open Spatial Datasets

Streets with an increasingly important place function play a significant role in the contemporary discourse on sustainable cities. The paper addresses urban streets that, observed from the urban planning perspective, have the potential to be transformed into either commercial or residential shared streets. After defining urban planning parameters identified as characteristic of shared streets, streets were quantified based on an analysis of the existing shared streets in Central Europe. By setting up distinctive scenarios that could help to detect specific types of streets, open spatial datasets were used for the mapping and identification of streets that could be transformed into shared streets. The methodology was tested on the example of the city of Maribor in Slovenia. The results of the research show that the selected urban planning parameters can help to identify streets with transformation potential, whereas the basic parameters, such as the building use and the length and width of the street, help us to better understand the urban fabric in which street spaces acquire new functions. The presented mapping method could serve as a testing tool for experts, planners, decision-makers, and the interested public to identify potential street transformations.

Assessment of Nutrient Expert Tool for Fertilizer Management in Rice at Fulbari, Chitwan, Nepal

A field trial was conducted at Fulbari, Chitwan in 2017 to evaluate the growth, productivity and economics of rice production under different nutrient management practices. Trial was laid out in a split-plot design with five replications. The main plot factor consisted of two rice varieties (Radha-4; improved variety and Arize Tej Gold; a popular Hybrid) and subplot factor consisted of five different nutrient management practices farmers’ applied dose (FAD;0-20:0-15:0-18 NPK kg ha-1), government recommended dose (GRD; 100:30:30 NPK kg ha-1), NARC recommended dose (NRD; 120:60:40 NPK kg ha-1), LCC based N and NE® based P, K dose (LCC-N+NE-P,K; 90-115:5-22:17-50 NPK kg ha-1 for Radha-4 and 90-140:5-36:38-73 NPK kg ha-1 for Arize Tej Gold), Nutrient Expert® based NPK dose (NED; 93-109:5-22:17-50 NPK kg ha-1 for Radha-4 and 118-125:5-36:38-73 NPK kg ha-1 for Arize Tej Gold). Data regarding biometrical, phenological, and yield attributes were recorded at regular intervals. Results revealed that different nutrient management practices had a significant influence on growth, yield attributes, and yield of rice. Number of effective tillers per square meter (215.62), number of grains per panicle (132.52), panicle length (27.02 cm) was recorded higher in LCC-N+NE-P, K. Similarly, a number of grains per panicle and panicle length in LCC-N+NE-P, K were statistically similar to NED. Significantly higher grain yield (5.19 Mt ha-1) and straw yield (6.43 Mt ha-1) were recorded in LCC-N+NE-P, K compared to FAD but statistically similar to NED. Higher Benefit-cost (B:C) ratio (2.41) was obtained in LCC-N + NE – P, K which was statistically similar to FAD and NED. Thus, the combined use of LCC and Nutrient Expert software for nutrient management in rice was found productive and profitable in western Chitwan.

Pollution potential of dumping sites on surface water quality in Ethiopia using leachate and comprehensive pollution indices.

Municipal solid waste disposed of in illegal dumpsites pollutes the surface and groundwater. However, accurately determining these pollution levels is typically challenging for practitioners and decision-makers in developing countries. The purpose of this study is to use the leachate pollution index (LPI) to assess the contamination potential of uncontrolled dumping sites along the course of the Kulfo River in Arba Minch, Ethiopia. The comprehensive pollution index (CPI) approach was also utilized to assess the suitability of Kulfo River water quality for aquatic species. Leachate samples were collected from four uncontrolled dumping sites along the Kulfo River's course and analyzed for fifteen leachate characteristics necessary to quantify the LPI sub-indices. Water samples were taken from three monitoring stations along the river and examined for aquatic species suitability. When the leachate parameters were compared to the Indian limit for discharge of treated leachate, it was found that dumping sites posed a considerable risk of pollution to adjacent water resources. The overall LPI ranged from 23.34 to 27.35, which is higher than the discharge standard LPI of 5.69, indicating that dumping sites can threaten the surrounding water resources and human health. Based on the rating scale of CPI, at all monitoring stations, the river resulted severely polluted. Finally, appropriate strategies to reduce the pollution and the related mismanagement of solid waste were discussed. Combining LPI and CPI methods can represent a crucial tool for experts and decision-makers in developing countries to evaluate the pollution potential of dumping sites and water resource monitoring.

Precision Nitrogen Management in Wheat at Rampur, Chitwan, Nepal

The application of blanket recommendation of nitrogen fertilizer leads to over or under fertilization. There is need to synchronize the N fertilizer application with plant demand. Field experiment was conducted during 2019 -2020 in Chitwan to assess the yield, nitrogen use efficiencies and economics of wheat production under precision N management compared with fixed time N management. Experiment was laid out in split plot design with sixteen treatments and three replications. The main plot treatments were varieties Vijay and Banganga and subplot treatments were three SPAD readings (≤35, ≤40, ≤45), two LCC readings (≤4, ≤5), Nutrient expert tool, fixed time nitrogen management(FTNM) with national recommended dose and control (zero N). The research result showed that varieties did not differ in yield and economics. Precision nitrogen management with SPAD≤45 and LCC≤5 consumed higher nitrogen doses and produced better yield attributes and yield (5585 and 5385 kgha-1 respectively) compared with FTNM. The agronomic use efficiency of nitrogen (AEN), recovery efficiency (REN), partial factor productivity (PFP were highest at LCC≤4 which consumed less nitrogen. SPAD≤35, LCC≤4 and NE treatments saved 15, 35 and 20 kgha-1 N respectively without compromising the yield obtained in FTNM. But, the benefit: cost ratio was highest at LCC≤5.Therefore, in terms of yield and profitability of wheat production, LCC≤5 is better than other treatments. The present national recommended dose of nitrogen to wheat crop is insufficient to achieve higher yield in Chitwan condition.

The Nationally Determined Contribution Expert Tool (NEXT): A Comprehensive Greenhouse Gas Accounting Tool to Support Annual Environmental Impact Assessment Over a 30-Year Time Series in the Agriculture, Forestry and Other Land Use Sector

The Nationally Determined Contribution Expert Tool (NEXT) is a greenhouse gas accounting tool developed by the Food and Agriculture Organization of the United Nations (FAO) to support annual environmental impact assessment for the Agriculture, Forestry and Other Land Use sector (AFOLU). It provides a 30-year time series of annual and cumulated estimates of carbon removal and greenhouse gas emission reductions from actions determined by Parties in their climate policies. NEXT was developed using the Intergovernmental Panel on Climate Change (IPCC) methodologies, and estimates can be made using either the IPCC 2006 guidelines or the IPCC 2019 refinement to the IPCC 2006 which are both complemented with the IPCC 2013 Wetlands Supplement. The tool was designed to provide results that directly respond to the provisions of the Enhanced Transparency Framework and support the Nationally Determined Contributions (NDC) development as required by the modalities, procedures and guidelines. NEXT provides a detailed temporal series of results and a wide set of indicators, including the social value of carbon, enabling a comprehensive environmental and economic overview of climate actions in achieving mitigation targets. The tool helps countries to interpret, track and scale up ambition of their NDCs which could ultimately inform the global stocktake of the Paris Agreement in a harmonized way.

Biopredictive tools for the development of injectable drug products

Introduction Biopredictive release tests are commonly used in the evaluation of oral medicines. They support decision-making in formulation development and allow predictions of the expected in-vivo performances. So far, there is limited experience in the application of these methodologies to injectable drug products. Areas covered Parenteral drug products cover a variety of dosage forms and administration sites including subcutaneous, intramuscular, and intravenous injections. In this area, developing biopredictive and biorelevant methodologies often confronts us with unique challenges and knowledge gaps. Here, we provide a formulation-centric approach and explain the key considerations and workflow when designing biopredictive assays. Also, we outline the key role of computational methods in achieving clinical relevance and put all considerations into context using liposomal nanomedicines as an example. Expert opinion Biopredictive tools are the need of the hour to exploit the tremendous opportunities of injectable drug products. A growing number of biopharmaceuticals such as peptides, proteins, and nucleic acids require different strategies and a better understanding of the influences on drug absorption. Here, our design strategy must maintain the balance of robustness and complexity required for effective formulation development.

Machine Learning Algorithms for Biophysical Classification of Lithuanian Lakes Based on Remote Sensing Data

Inland waters are dynamic systems that are under pressure from anthropogenic activities, thus constant observation of these waters is essential. Remote sensing provides a great opportunity to have frequent observations of inland waters. The aim of this study was to create a data-driven model that uses a machine learning algorithm and Sentinel-2 data to classify lake observations into four biophysical classes: Clear, Moderate, Chla-dominated, and Turbid. We used biophysical variables such as water transparency, chlorophyll concentration, and suspended matter to define these classes. We tested six machine learning algorithms that use spectral features of lakes as input and chose random forest classifiers, which yielded the most accurate results. We applied our two-step model on 19,292 lake spectra for the years 2015–2020, from 226 lakes. The prevalent class in 67% of lakes was Clear, while 19% of lakes were likely affected by strong algal blooms (Chla-dominated class). The models created in this study can be applied to lakes in other regions where similar lake classes are found. Biophysical lake classification using Sentinel-2 MSI data can help to observe long-term and short-term changes in lakes, thus it can be a useful tool for water management experts and for the public.

Multivariate statistical approach and machine learning for the evaluation of biogeographical ancestry inference in the forensic field

The biogeographical ancestry (BGA) of a trace or a person/skeleton refers to the component of ethnicity, constituted of biological and cultural elements, that is biologically determined. Nowadays, many individuals are interested in exploring their genealogy, and the capability to distinguish biogeographic information about population groups and subgroups via DNA analysis plays an essential role in several fields such as in forensics. In fact, for investigative and intelligence purposes, it is beneficial to inference the biogeographical origins of perpetrators of crimes or victims of unsolved cold cases when no reference profile from perpetrators or database hits for comparative purposes are available. Current approaches for biogeographical ancestry estimation using SNPs data are usually based on PCA and Structure software. The present study provides an alternative method that involves multivariate data analysis and machine learning strategies to evaluate BGA discriminating power of unknown samples using different commercial panels. Starting from 1000 Genomes project, Simons Genome Diversity Project and Human Genome Diversity Project datasets involving African, American, Asian, European and Oceania individuals, and moving towards further and more geographically restricted populations, powerful multivariate techniques such as Partial Least Squares-Discriminant Analysis (PLS-DA) and machine learning techniques such as XGBoost were employed, and their discriminating power was compared. PLS-DA method provided more robust classifications than XGBoost method, showing that the adopted approach might be an interesting tool for forensic experts to infer BGA information from the DNA profile of unknown individuals, but also highlighting that the commercial forensic panels could be inadequate to discriminate populations at intra-continental level.

Personalized remotely guided preventive exercise therapy for a healthy heart: protocol and design of the PRIORITY study

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Research Foundation – Flanders (FWO) Introduction Approximately half of the heart failure population has heart failure with preserved ejection fraction (HFpEF), a chronic disease starting with cardiovascular risk factors such as hypertension, diabetes and obesity (stage A) which can further emerge in a structural heart disease without (stage B) or with (stage C) signs or symptoms. Structured exercise therapy is recommended as a Class IA intervention in national and international guidelines and, as such, should be offered to all patients. Yet, in current practice, exercise therapy is often only offered within a secondary prevention program. At present, there exists no comprehensive preventive care program that includes structured exercise for patients in the early stages of heart failure, when cardiovascular risk factors are present, but cardiac remodeling and dysfunction might still be reversible or even preventable. Purpose PRIORITY aims to investigate the use of remotely guided exercise therapy as a preventive clinical and cost-effective treatment in the HFpEF continuum. This includes both prevention of progression of asymptomatic diastolic dysfunction towards symptomatic HFpEF (= primary prevention) and delaying progression of symptomatic HFpEF (= secondary prevention). Methods A randomized controlled multicenter trial will be conducted in 450 patients (men and women, aged 35-80 years) with heart failure (n = 180 stage A, 180 stage B, 90 stage C). Participants are being recruited from 3 different hospitals and the general population during a 16-month period which started in September 2021. Patients will be randomized (1:1) to usual care or to the PRIORITY exercise intervention (i.e. a combination of supervised with remotely guided home-based training sessions). Training prescription is based on the EXPERT tool and includes person-tailored endurance and dynamic strength training. During one year, participants will receive 18 supervised exercise sessions supplemented with a structured progressive home-based exercise program. Outcomes will be assessed at baseline, 4 months, one and two-years. Primary outcome is the proportion of patients with a clinically relevant improvement in peak oxygen uptake at one-year. Secondary outcomes include vascular health, muscle metabolism, change in electrocardiographic parameters and physical fitness parameters (muscle strength, body composition). Further, big data of physical activity collected during the trial will be used to develop models using machine-learning algorithms which can predict physical activity uptake and changes in fitness to facilitate the creation of more personalized interventions and better tailored exercise prescription. Conclusion We anticipate that the PRIORITY study will contribute to better prevention of heart failure thanks to an early easily accessible person-tailored exercise intervention.

On the Quantum Performance Evaluation of Two Distributed Quantum Architectures

Distributed quantum applications impose requirements on the quality of the quantum states that they consume. When analyzing architecture implementations of quantum hardware, characterizing this quality forms an important factor in understanding their performance. Fundamental characteristics of quantum hardware lead to inherent tradeoffs between the quality of states and traditional performance metrics such as throughput. Furthermore, any real-world implementation of quantum hardware exhibits time-dependent noise that degrades the quality of quantum states over time. Here, we study the performance of two possible architectures for interfacing a quantum processor with a quantum network. The first corresponds to the current experimental state of the art in which the same device functions both as a processor and a network device. The second corresponds to a future architecture that separates these two functions over two distinct devices. We model these architectures as continuous-time Markov chains and compare their quality of executing quantum operations and producing entangled quantum states as functions of their memory lifetimes, as well as the time that it takes to perform various operations within each architecture. As an illustrative example, we apply our analysis to architectures based on Nitrogen- Vacancy centers in diamond, where we find that for presentday device parameters one architecture is more suited to computation-heavy applications, and the other for networkheavy ones. We validate our analysis with the quantum network simulator NetSquid. Besides the detailed study of these architectures, a novel contribution of our work are several formulas that connect an understanding of waiting time distributions to the decay of quantum quality over time for the most common noise models employed in quantum technologies. This provides a valuable new tool for performance evaluation experts, and its applications extend beyond the two architectures studied in this work.

Optimization of parameters for needle cut index using TOPSIS method

The influence of loop length, stitches per inch, and sewing needle type on needle cut index in 100% cotton single jersey fabric has been studied considering factorial design. Further optimization of factors using Design Expert Tool has been done followed by ranking the optimized solution through TOPSIS method along with the confirmatory test. The results show that sewing needle type has the highest contribution with 29.83% followed by the 23.08% for stitches per inch and 11.95% in the case of loop length as far as needle cut index in the course direction is concerned. In the wale direction, sewing needle type has the highest contribution with 50.45% followed by the 13.30% for loop length and 10.77% for stitches per inch. It has also been observed that SES needle type is generating more needle cut as compared to SUK and SAN type needles. It is confirmed that the error percentage has been the lowest for highest rank solutions and subsequently the error increases with decreasing rank in terms of closeness coefficient. The present study is expected to be helpful for garment industrialists in minimising the needle cut defect among knitted garments and improve the quality of producing seam

A multi-sequences MRI deep framework study applied to glioma classfication.

Glioma is one of the most important central nervous system tumors, ranked 15th in the most common cancer for men and women. Magnetic Resonance Imaging (MRI) represents a common tool for medical experts to the diagnosis of glioma. A set of multi-sequences from an MRI is selected according to the severity of the pathology. Our proposed approach aims moreto create a computer-aided system that is capable of helping morethe expert diagnose the brain gliomas. moreWe propose a supervised learning regime based on a convolutional neural network based framework and transfer learning techniques. Our research morefocuses on the performance of different pre-trained deep learning models with respect to different MRI sequences. We highlight the best combinations of such model-MRI sequence couple for our specific task of classifying healthy brain against brain with glioma. moreWe also propose to visually analyze the extracted deep features for studying the existing relation of the MRI sequences and models. This interpretability analysis gives some hints for medical expert to understand the diagnosis made by the models. Our study is based on the well-known BraTS datasets including multi-sequence images and expert diagnosis.

Brain Tumor Classification Using a Combination of Variational Autoencoders and Generative Adversarial Networks.

Brain tumors are a pernicious cancer with one of the lowest five-year survival rates. Neurologists often use magnetic resonance imaging (MRI) to diagnose the type of brain tumor. Automated computer-assisted tools can help them speed up the diagnosis process and reduce the burden on the health care systems. Recent advances in deep learning for medical imaging have shown remarkable results, especially in the automatic and instant diagnosis of various cancers. However, we need a large amount of data (images) to train the deep learning models in order to obtain good results. Large public datasets are rare in medicine. This paper proposes a framework based on unsupervised deep generative neural networks to solve this limitation. We combine two generative models in the proposed framework: variational autoencoders (VAEs) and generative adversarial networks (GANs). We swap the encoder–decoder network after initially training it on the training set of available MR images. The output of this swapped network is a noise vector that has information of the image manifold, and the cascaded generative adversarial network samples the input from this informative noise vector instead of random Gaussian noise. The proposed method helps the GAN to avoid mode collapse and generate realistic-looking brain tumor magnetic resonance images. These artificially generated images could solve the limitation of small medical datasets up to a reasonable extent and help the deep learning models perform acceptably. We used the ResNet50 as a classifier, and the artificially generated brain tumor images are used to augment the real and available images during the classifier training. We compared the classification results with several existing studies and state-of-the-art machine learning models. Our proposed methodology noticeably achieved better results. By using brain tumor images generated artificially by our proposed method, the classification average accuracy improved from 72.63% to 96.25%. For the most severe class of brain tumor, glioma, we achieved 0.769, 0.837, 0.833, and 0.80 values for recall, specificity, precision, and F1-score, respectively. The proposed generative model framework could be used to generate medical images in any domain, including PET (positron emission tomography) and MRI scans of various parts of the body, and the results show that it could be a useful clinical tool for medical experts.

What's left in the tank? Identification of non-ascribed aquarium's coral collections with DNA barcodes as part of an integrated diagnostic approach.

The unprecedented threats to coral reef ecosystems from global climate change require an urgent response from the aquarium community, which is becoming an increasingly vital coral conservation resource. Unfortunately, many hermatypic corals in aquaria are not identified to species level, which hinders assessment of their conservation significance. Traditional methods of species identification using morphology can be challenging, especially to non-taxonomists. DNA barcoding is an option for species identification of Scleractinian corals, especially when used in concert with morphology-based assessment. This study uses DNA barcodes to try to identify aquarium specimens of the diverse reef-forming genus Acropora from 127 samples. We identified to our best current knowledge, to species name 44% of the analysed samples and provided provisional identification for 80% of them (101/127, in the form of a list of species names with associate confidence values). We highlighted a sampling bias in public nucleotide sequences repertories (e.g. GenBank) towards more charismatic and more studied species, even inside a well-studied genus like Acropora. In addition, we showed a potential “single observer” effect with over a quarter of the reference sequences used for these identifications coming from the same study. We propose the use of barcoding and query matching as an additional tool for taxonomic experts and general aquarists, as an additional tool to increase their chances of making high confidence species-level identifications. We produce a standardised and easily repeatable methodology to increase the capacity of aquariums and other facilities to assess non-ascribed species, emphasising the value of integrating this approach with morphological identification optimising usage of authoritative identification guides and expert opinion.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12686-021-01250-3.