Semi-automated Data Research Articles

Towards routine 3D characterization of intact mesoscale samples by multi-scale and multimodal scanning X-ray tomography

Non-invasive multi-scale and multimodal 3D characterization of heterogeneous or hierarchically structured intact mesoscale samples is of paramount importance in tackling challenging scientific problems. Scanning hard X-ray tomography techniques providing simultaneous complementary 3D information are ideally suited to such studies. However, the implementation of a robust on-site workflow remains the bottleneck for the widespread application of these powerful multimodal tomography methods. In this paper, we describe the development and implementation of such a robust, holistic workflow, including semi-automatic data reconstruction. Due to its flexibility, our approach is especially well suited for on-the-fly tuning of the experiments to study features of interest progressively at different length scales. To demonstrate the performance of the method, we studied, across multiple length scales, the elemental abundances and morphology of two complex biological systems, Arabidopsis plant seeds and mouse renal papilla samples. The proposed approach opens the way towards routine multimodal 3D characterization of intact samples by providing relevant information from pertinent sample regions in a wide range of scientific fields such as biology, geology, and material sciences.

A holistic digital twin simulation framework for industrial facilities: BIM-based data acquisition for building energy modeling

Energy and resource efficiency as well as reduction of emissions are nowadays significant objectives for production companies. Industry 4.0, through extensive digitalization along the value chain, enables the achievement of these objectives not only in the construction of new facilities but also in existing facilities as well. This requires an interdisciplinary approach, extending over production and logistic processes as well as the building, technical building services, and energy supply systems, consolidated through integrated modeling and simulation-based optimization. The research question this study addresses is how to digitally couple these subsystems and optimize the overall system’s performance in terms of energy and resource efficiency, by distancing from silo-field thinking while using an integrated analysis approach. The article briefly presents a holistic modeling and simulation framework, utilizing modular digital twins (DTs) of all elements that may constitute a given industrial unit. The integration of multiple DTs of these subsystems in a hybrid (continuous and discrete) simulation forms a holistic DT ecosystem of an existing facility. The particular focus of the study is the building representation in this DT ecosystem for energy-efficient production. Based on a methodology including hybrid simulation, building information modeling (BIM), and visual programming, a semi-automated data acquisition workflow was proposed. The hybrid simulation is based on Discrete Event System Specification (DEVS) formalism, where the building is incorporated as a building energy model (BEM). Within the abstracted representation of the overall system, the article explores the possibilities of parametrizing the DT of the building, interconnected with the rest of the factory elements, by acquiring information directly from existing BIM models. Through a comparative case study, the proposed workflow is compared to a manual one in terms of integrity and benefits. The study’s contribution lies in: 1) the detection of the required building level of abstraction for a holistic DT ecosystem, 2) the definition of the interconnections between the building-related counterparts and the rest of the virtual environment as well as the data required for their parameterization, and 3) proposing a semi-automated workflow via virtual programming, for BIM-based creation of the building model within a holistic DT ecosystem.

Machine Learning and Data Cleaning: Which Serves the Other?

The last few years witnessed significant advances in building automated or semi-automated data quality, data cleaning and data integration systems powered by machine learning (ML). In parallel, large deployment of ML systems in business, science, environment and various other areas started to realize the strong dependency on the quality of the input data to these ML models to get reliable predictions or insights. That dual relationship between ML and data cleaning has been addressed by many recent research works under terms such as “Data cleaning for ML” and “ML for automating data cleaning and data preparation”. In this article, we highlight this symbiotic relationship between ML and data cleaning and discuss few challenges that require collaborative efforts of multiple research communities.

Semi-automatic data annotation based on feature-space projection and local quality metrics: An application to cerebral emboli characterization.

We propose a semi-supervised learning approach to annotate a dataset with reduced requirements for manual annotation and with controlled annotation error. The method is based on feature-space projection and label propagation using local quality metrics. First, an auto-encoder extracts the features of the samples in an unsupervised manner. Then, the extracted features are projected by a t-distributed stochastic neighbor embedding algorithm into a two-dimensional (2D) space. A selection of the best 2D projection is introduced based on the silhouette score. The expert annotator uses the obtained 2D representation to manually label samples. Finally, the labels of the labeled samples are propagated to the unlabeled samples using a K-nearest neighbor strategy and local quality metrics. We compare our method against semi-supervised optimum-path forest and K-nearest neighbor label propagation (without considering local quality metrics). Our method achieves state-of-the-art results on three different datasets by labeling more than 96% of the samples with an annotation error from 7% to 17%. Additionally, our method allows to control the trade-off between annotation error and number of labeled samples. Moreover, we combine our method with robust loss functions to compensate for the label noise introduced by automatic label propagation. Our method allows to achieve similar, and even better, classification performances compared to those obtained using a fully manually labeled dataset, with up to 6% in terms of classification accuracy.

A Semi-Automatic Data Cleaning & Coding Tool for Chinese Clinical Data Standardization.

The clinical data often have limited usefulness because of the diversified expression. Chinese clinical data standardization can improve the usability of clinical data. The complexity of data cleaning and coding for Chinese clinical data prompted the turn of low-effective manual coding into the computer-aided tool. This study established the universal data cleaning and coding process and tool for Chinese clinical data standardization, which can greatly improve human efficiency. The process included the preprocessing, text similarity algorithm, and manual review. The standardization process proved effective for the diagnosis, drug, and examination data standardization task and can be used gradually in other clinical domains. The semi-automatic data cleaning and coding can reduce the half time for standardization, and it was used in hospitals in Beijing.

Modern challenges of property market analysis- homogeneous areas determination

Effective comprehension of highly complex and spatially heterogenous property market requires its’ appropriate recognition. One of the most critical steps in property analyses and valuation procedures is the identification of the sub-markets as the fundamental comparable units. The biggest challenge in this case is to define the criteria of the basis indicating the similarity (homogeneity) of property markets area. The objective of the study was to propose methodology (called “HO-MAR”) that enables objective identification of the homogenous (similar/comparable) areas (zones) that could indicate location of similar groups of property transactions (representative properties) used either in individual valuations or AVMs. The authors propose utilization of automated solutions based on robust geo-estimation that enables high efficacy of property submarkets identification. Robust geo-estimation is guaranteed by merging semi-automated data mining methods (e.g. entropy theory, rough set theory, fuzzy logic) and geoprocessing activities (Gauss filter, geocoding and reverse geocoding, tessellation model with mutual spatial overlapping) concerning spatial relation database application.

Accuracy and Efficiency of Machine Learning-Assisted Risk-of-Bias Assessments in "Real-World" Systematic Reviews : A Noninferiority Randomized Controlled Trial.

Automation is a proposed solution for the increasing difficulty of maintaining up-to-date, high-quality health evidence. Evidence assessing the effectiveness of semiautomated data synthesis, such as risk-of-bias (RoB) assessments, is lacking. To determine whether RobotReviewer-assisted RoB assessments are noninferior in accuracy and efficiency to assessments conducted with human effort only. Two-group, parallel, noninferiority, randomized trial. (Monash Research Office Project 11256). Health-focused systematic reviews using Covidence. Systematic reviewers, who had not previously used RobotReviewer, completing Cochrane RoB assessments between February 2018 and May 2020. In the intervention group, reviewers received an RoB form prepopulated by RobotReviewer; in the comparison group, reviewers received a blank form. Studies were assigned in a 1:1 ratio via simple randomization to receive RobotReviewer assistance for either Reviewer 1 or Reviewer 2. Participants were blinded to study allocation before starting work on each RoB form. Co-primary outcomes were the accuracy of individual reviewer RoB assessments and the person-time required to complete individual assessments. Domain-level RoB accuracy was a secondary outcome. Of the 15 recruited review teams, 7 completed the trial (145 included studies). Integration of RobotReviewer resulted in noninferior overall RoB assessment accuracy (risk difference, -0.014 [95% CI, -0.093 to 0.065]; intervention group: 88.8% accurate assessments; control group: 90.2% accurate assessments). Data were inconclusive for the person-time outcome (RobotReviewer saved 1.40 minutes [CI, -5.20 to 2.41 minutes]). Variability in user behavior and a limited number of assessable reviews led to an imprecise estimate of the time outcome. In health-related systematic reviews, RoB assessments conducted with RobotReviewer assistance are noninferior in accuracy to those conducted without RobotReviewer assistance. University College London and Monash University.

AutoPoD-Mobile—Semi-Automated Data Population Using Case-like Scenarios for Training and Validation in Mobile Forensics

The complexity and constant changes in mobile forensics require special training of investigators with datasets that are as realistic as possible. Even today, the generation of training data is almost exclusively done manually. This paper presents a novel open-source framework called AutoPoD-Mobile. The framework supports the creation of case-based scenarios. Even more, the semi-automated provision of datasets for mobile forensics is enabled. Thus, the behavior of suspects interacting with each other can be simulated. The result combines mobile device data from normal device usage and case-related information. This way helps validate mobile forensic tools, test new techniques, and create realistic training datasets in the mobile forensics domain. The results of a proof-of-concept trial in a realistic deployment environment will also be presented. The paper concludes with a discussion of the results and identifies options for future improvements.

Semantic keypoint-based pose estimation from single RGB frames

This paper presents an approach to estimating the continuous 6-DoF pose of an object from a single RGB image. The approach combines semantic keypoints predicted by a convolutional network (convnet) with a deformable shape model. Unlike prior investigators, we are agnostic to whether the object is textured or textureless, as the convnet learns the optimal representation from the available training-image data. Furthermore, the approach can be applied to instanceand class-based pose recovery. Additionally, we accompany our main pipeline with a technique for semi-automatic data generation from unlabeled videos. This procedure allows us to train the learnable components of our method with minimal manual intervention in the labeling process. Empirically, we show that our approach can accurately recover the 6-DoF object pose for both instance- and class-based scenarios even against a cluttered background. We apply our approach both to several, existing, large-scale datasets - including PASCAL3D+, LineMOD-Occluded, YCB-Video, and TUD-Light - and, using our labeling pipeline, to a new dataset with novel object classes that we introduce here. Extensive empirical evaluations show that our approach is able to provide pose estimation results comparable to the state of the art.

Semi-automated data collection from electronic health records in a stroke unit in Brazil.

There is a high demand for stroke patient data in the public health systems of middle and low-income countries. To develop a stroke databank for integrating clinical or functional data and benchmarks from stroke patients. This was an observational, cross-sectional, prospective study. A tool was developed to collect all clinical data during hospitalizations due to stroke, using an electronic editor of structured forms that was integrated with electronic medical records. Validation of fields in the electronic editor was programmed using a structured query language (SQL). To store the results from SQL, a virtual table was created and programmed to update daily. To develop an interface between the data and user, the Embarcadero Delphi software and the DevExpress component were used to generate the information displayed on the screen. The data were extracted from the fields of the form and also from cross-referencing of other information from the computerized system, including patients who were admitted to the stroke unit. The database was created and integrated with the hospital electronic system, thus allowing daily data collection. Quality indicators (benchmarks) were created in the database for the system to track and perform decision-making in conjunction with healthcare service managers, which resulted in improved processes and patient care after a stroke. An intelligent portal was created, in which the information referring to the patients was accessible. Based on semi-automated data collection, it was possible to create a dynamic and optimized Brazilian stroke databank.

Evaluation of a semi-automated data extraction tool for public health literature-based reviews: Dextr

There has been limited development and uptake of machine-learning methods to automate data extraction for literature-based assessments. Although advanced extraction approaches have been applied to some clinical research reviews, existing methods are not well suited for addressing toxicology or environmental health questions due to unique data needs to support reviews in these fields. To develop and evaluate a flexible, web-based tool for semi-automated data extraction that: 1) makes data extraction predictions with user verification, 2) integrates token-level annotations, and 3) connects extracted entities to support hierarchical data extraction. Dextr was developed with Agile software methodology using a two-team approach. The development team outlined proposed features and coded the software. The advisory team guided developers and evaluated Dextr's performance on precision, recall, and extraction time by comparing a manual extraction workflow to a semi-automated extraction workflow using a dataset of 51 environmental health animal studies. The semi-automated workflow did not appear to affect precision rate (96.0% vs. 95.4% manual, p=0.38), resulted in a small reduction in recall rate (91.8% vs. 97.0% manual, p<0.01), and substantially reduced the median extraction time (436s vs. 933s per study manual, p<0.01) compared to a manual workflow. Dextr provides similar performance to manual extraction in terms of recall and precision and greatly reduces data extraction time. Unlike other tools, Dextr provides the ability to extract complex concepts (e.g., multiple experiments with various exposures and doses within a single study), properly connect the extracted elements within a study, and effectively limit the work required by researchers to generate machine-readable, annotated exports. The Dextr tool addresses data-extraction challenges associated with environmental health sciences literature with a simple user interface, incorporates the key capabilities of user verification and entity connecting, provides a platform for further automation developments, and has the potential to improve data extraction for literature reviews in this and other fields.

Integration of passenger and freight transport: A concept-centric literature review

The integration of passenger and freight transport has been the subject of debate among scholars from the beginning of the century, with a peak observed in the last five years. Considering the relatively recent interest in the topic, most authors have highlighted the heterogeneous and explorative approaches adopted so far, indicating a lack of systematic analyses. This is confirmed by the different names given to the same concept (e.g., co-modality, cargo hitching, system with mixed passengers and goods, share-a-ride, integrated passenger and freight logistics, and collaborative passenger and freight transport). This study conducts a comprehensive literature review based on the scientific contributions indexed in Scopus and selected through a semiautomatic data extraction. First, a descriptive analysis is conducted, which includes types of publication, geographical areas, sources of publication, research methods, and research design adopted. Then, a text mining analysis identifies the main content-related aspects, including a semantic investigation of the most frequently occurring terms, their clustering in homogeneous groups, the transport means considered, and the territorial scales that have been investigated. This analysis is used to define the future challenges related to the topic, which span from the provision of more robust quantitative analyses (studies providing real data and adopting ad-hoc models are still very limited) to policy-related issues. However, the definition of a normative framework that integrates both systems is essential to deal with passenger–freight transport in a combined manner.

Creating efficiencies in the extraction of data from randomized trials: a prospective evaluation of a machine learning and text mining tool

BackgroundMachine learning tools that semi-automate data extraction may create efficiencies in systematic review production. We evaluated a machine learning and text mining tool’s ability to (a) automatically extract data elements from randomized trials, and (b) save time compared with manual extraction and verification.MethodsFor 75 randomized trials, we manually extracted and verified data for 21 data elements. We uploaded the randomized trials to an online machine learning and text mining tool, and quantified performance by evaluating its ability to identify the reporting of data elements (reported or not reported), and the relevance of the extracted sentences, fragments, and overall solutions. For each randomized trial, we measured the time to complete manual extraction and verification, and to review and amend the data extracted by the tool. We calculated the median (interquartile range [IQR]) time for manual and semi-automated data extraction, and overall time savings.ResultsThe tool identified the reporting (reported or not reported) of data elements with median (IQR) 91% (75% to 99%) accuracy. Among the top five sentences for each data element at least one sentence was relevant in a median (IQR) 88% (83% to 99%) of cases. Among a median (IQR) 90% (86% to 97%) of relevant sentences, pertinent fragments had been highlighted by the tool; exact matches were unreliable (median (IQR) 52% [33% to 73%]). A median 48% of solutions were fully correct, but performance varied greatly across data elements (IQR 21% to 71%). Using ExaCT to assist the first reviewer resulted in a modest time savings compared with manual extraction by a single reviewer (17.9 vs. 21.6 h total extraction time across 75 randomized trials).ConclusionsUsing ExaCT to assist with data extraction resulted in modest gains in efficiency compared with manual extraction. The tool was reliable for identifying the reporting of most data elements. The tool’s ability to identify at least one relevant sentence and highlight pertinent fragments was generally good, but changes to sentence selection and/or highlighting were often required.Protocolhttps://doi.org/10.7939/DVN/RQPJKS

A Scalable Method to Study Neuronal Survival in Primary Neuronal Culture with Single-cell and Real-Time Resolution.

Neuronal loss is at the core of many neuropathologies, including stroke, Alzheimer's disease, and Parkinson's disease. Different methods were developed to study the process of neuronal survival upon cytotoxic stress. Most methods are based on biochemical approaches that do not allow single-cell resolution or involve complex and costly methodologies. Presented here is a versatile, inexpensive, and effective experimental paradigm to study neuronal survival. This method takes advantage of sparse fluorescent labeling of the neurons followed by live imaging and automated quantification. To this aim, the neurons are electroporated to express fluorescent markers and co-cultured with non-electroporated neurons to easily regulate cell density and increase survival. Sparse labeling by electroporation allows a simple and robust automated quantification. In addition, fluorescent labeling can be combined with the co-expression of a gene of interest to study specific molecular pathways. Here, we present a model of stroke as a neurotoxic model, namely, the oxygen-glucose deprivation (OGD) assay, which was performed in an affordable and robust homemade hypoxic chamber. Finally, two different workflows are described using IN Cell Analyzer 2200 or the open-source ImageJ for image analysis for semi-automatic data processing. This workflow can be easily adapted to different experimental models of toxicity and scaled up for high-throughput screening. In conclusion, the described protocol provides an approachable, affordable, and effective in vitro model of neurotoxicity, which can be suitable for testing the roles of specific genes and pathways in live imaging and for high-throughput drug screening.

Global transpiration data from sap flow measurements: the SAPFLUXNET database

Abstract. Plant transpiration links physiological responses of vegetation to water supply and demand with hydrological, energy, and carbon budgets at the land–atmosphere interface. However, despite being the main land evaporative flux at the global scale, transpiration and its response to environmental drivers are currently not well constrained by observations. Here we introduce the first global compilation of whole-plant transpiration data from sap flow measurements (SAPFLUXNET, https://sapfluxnet.creaf.cat/, last access: 8 June 2021). We harmonized and quality-controlled individual datasets supplied by contributors worldwide in a semi-automatic data workflow implemented in the R programming language. Datasets include sub-daily time series of sap flow and hydrometeorological drivers for one or more growing seasons, as well as metadata on the stand characteristics, plant attributes, and technical details of the measurements. SAPFLUXNET contains 202 globally distributed datasets with sap flow time series for 2714 plants, mostly trees, of 174 species. SAPFLUXNET has a broad bioclimatic coverage, with woodland/shrubland and temperate forest biomes especially well represented (80 % of the datasets). The measurements cover a wide variety of stand structural characteristics and plant sizes. The datasets encompass the period between 1995 and 2018, with 50 % of the datasets being at least 3 years long. Accompanying radiation and vapour pressure deficit data are available for most of the datasets, while on-site soil water content is available for 56 % of the datasets. Many datasets contain data for species that make up 90 % or more of the total stand basal area, allowing the estimation of stand transpiration in diverse ecological settings. SAPFLUXNET adds to existing plant trait datasets, ecosystem flux networks, and remote sensing products to help increase our understanding of plant water use, plant responses to drought, and ecohydrological processes. SAPFLUXNET version 0.1.5 is freely available from the Zenodo repository (https://doi.org/10.5281/zenodo.3971689; Poyatos et al., 2020a). The “sapfluxnetr” R package – designed to access, visualize, and process SAPFLUXNET data – is available from CRAN.

Design and Development of a Prototype Heads-Up Display: Supporting Context-Aware, Semi-Automated, Hands-Free Medical Documentation

Military and civilian medical personnel across all echelons of medical care play a critical role in evaluating, caring for, and treating casualties. Accurate medical documentation is critical to effective, coordinated care and positive patient outcomes. We describe our prototype, Context-Aware Procedure Support Tools and User Interfaces for Rapid and Effective Workflows (CAPTURE). Leveraging human factors and usercentered design methods, and advanced artificial intelligence and computer vision capabilities, CAPTURE was designed to enable Tactical Combat Causality Care (TCCC) providers to more efficiently and effectively input critical medical information through hands-free interaction techniques and semiautomated data capture methods. We designed and prototyped a heads-up display that incorporates: multimodal interfaces, including augmented reality-based methods for input and information display to support visual image capture and heads-up interaction; post-care documentation support (e.g., artifacts to support post-care review and documentation); context-aware active and passive data capture methods, specifically natural language interpretation using systemic functional grammars; and computer vision technologies for semi-automated data capture capabilities. During the course of this project we encountered challenges towards effective design which fall into three main categories: (1) challenges related to designing novel multimodal interfaces; (2) technical challenges related to software and hardware development to meet design needs; and (3) challenges as a result of domain characteristics and operational constraints. We discuss how we addressed some of these challenges and provide additional considerations necessary for future research regarding next generation technology design for medical documentation in the field.

Semi-Automated Glycoproteomic Data Analysis of LC-MS Data Using GlycopeptideGraphMS in Process Development of Monoclonal Antibody Biologics.

The glycosylation of antibody-based proteins is vital in translating the right therapeutic outcomes of the patient. Despite this, significant infrastructure is required to analyse biologic glycosylation in various unit operations from biologic development, process development to QA/QC in bio-manufacturing. Simplified mass spectrometers offer ease of operation as well as the portability of method development across various operations. Furthermore, data analysis would need to have a degree of automation to relay information back to the manufacturing line. We set out to investigate the applicability of using a semiautomated data analysis workflow to investigate glycosylation in different biologic development test cases. The workflow involves data acquisition using a BioAccord LC-MS system with a data-analytical tool called GlycopeptideGraphMS along with Progenesis QI to semi-automate glycoproteomic characterisation and quantitation with a LC-MS1 dataset of a glycopeptides and peptides. Data analysis which involved identifying glycopeptides and their quantitative glycosylation was performed in 30 min with minimal user intervention. To demonstrate the effectiveness of the antibody and biologic glycopeptide assignment in various scenarios akin to biologic development activities, we demonstrate the effectiveness in the filtering of IgG1 and IgG2 subclasses from human serum IgG as well as innovator drugs trastuzumab and adalimumab and glycoforms by virtue of their glycosylation pattern. We demonstrate a high correlation between conventional released glycan analysis with fluorescent tagging and glycopeptide assignment derived from GraphMS. GraphMS workflow was then used to monitor the glycoform of our in-house trastuzumab biosimilar produced in fed-batch cultures. The demonstrated utility of GraphMS to semi-automate quantitation and qualitative identification of glycopeptides proves to be an easy data analysis method that can complement emerging multi-attribute monitoring (MAM) analytical toolsets in bioprocess environments.

Semi-Automated Data Processing and Semi-Supervised Machine Learning for the Detection and Classification of Water-Column Fish Schools and Gas Seeps with a Multibeam Echosounder.

Multibeam echosounders are widely used for 3D bathymetric mapping, and increasingly for water column studies. However, they rapidly collect huge volumes of data, which poses a challenge for water column data processing that is often still manual and time-consuming, or affected by low efficiency and high false detection rates if automated. This research describes a comprehensive and reproducible workflow that improves efficiency and reliability of target detection and classification, by calculating metrics for target cross-sections using a commercial software before feeding into a feature-based semi-supervised machine learning framework. The method is tested with data collected from an uncalibrated multibeam echosounder around an offshore gas platform in the Adriatic Sea. It resulted in more-efficient target detection, and, although uncertainties regarding user labelled training data need to be underlined, an accuracy of 98% in target classification was reached by using a final pre-trained stacking ensemble model.

Automatic image annotation for fluorescent cell nuclei segmentation.

Dataset annotation is a time and labor-intensive task and an integral requirement for training and testing deep learning models. The segmentation of images in life science microscopy requires annotated image datasets for object detection tasks such as instance segmentation. Although the amount of annotated image data has been steadily reduced due to methods such as data augmentation, the process of manual or semi-automated data annotation is the most labor and cost intensive task in the process of cell nuclei segmentation with deep neural networks. In this work we propose a system to fully automate the annotation process of a custom fluorescent cell nuclei image dataset. By that we are able to reduce nuclei labelling time by up to 99.5%. The output of our system provides high quality training data for machine learning applications to identify the position of cell nuclei in microscopy images. Our experiments have shown that the automatically annotated dataset provides coequal segmentation performance compared to manual data annotation. In addition, we show that our system enables a single workflow from raw data input to desired nuclei segmentation and tracking results without relying on pre-trained models or third-party training datasets for neural networks.

Extraction of forest inventory parameters using handheld mobile laser scanning: A case study from Trabzon, Turkey

Forest inventory (FI) surveys are cumbersome when field measurements are performed by manual means. We propose a semi-automated data collection approach using handheld mobile laser scanning (HMLS) to estimate and map key FI parameters. To this end, machine learning (e.g., random forest classifier for tree detection) and innovative algorithms (e.g., ellipse fitting for diameter estimation of noncircular trees) were used for the first time in FI surveying. After surveying nine plots, we compared HMLS-derived data against the field reference. HMLS-derived tree diameters (DBHs) were strongly correlated with the reference data at the single-tree level (r= 0.93–0.99; p< 0.001). At the plot level, HMLS slightly overestimated DBHs in complex plots due to the influence of undergrowth and creepers on trunks. Yet, no statistically significant difference was found between the two datasets (p> 0.05). Overall, HMLS was concluded as efficient and effective tool for FIs, even if used alone.