Mapping Evaluation Research Articles

Clinical Evaluation of Non-Invasive Mapping of Premature Ventricular Contractions by Merging Magnetocardiography and Computed Tomography

Clinical Evaluation of Non-Invasive Mapping of Premature Ventricular Contractions by Merging Magnetocardiography and Computed Tomography

COMPARISION OF RICE PRICE PREDICTION RESULTS IN EAST JAVA USING FOURIER SERIES ESTIMATOR AND GAUSSIAN KERNEL ESTIMATOR SIMULTANEOUSLY

Extreme weather changes and the El Nino phenomenon in 2023 will cause drought, resulting in a decrease in rice production and an increase in rice prices. It has significantly impacted East Java Province as it is the most extensive rice supplier in Indonesia. This study aims to predict the price of rice with six different qualities using the Fourier series estimator and Gaussian kernel function simultaneously. The results show that the Gaussian kernel method, with a bandwidth value of 1, produces a better model with a MAPE value of 0.228259% than the sine function Fourier series method in predicting rice prices based on six different qualities. The prediction results using the Gaussian kernel function method are categorized as highly accurate because they are less than 10%. This research accelerates the realization of SDG 2 related to "Zero Hunger" through government policies to control the high price of rice in Indonesia. Recommendations that can be given through the research results include cooperation with the government, which can help access information and resources needed to manage price risks.

Real Time Traffic Sign Recognition Using Integrated Camera Sensors and YOLOV8 Algorithm

Traffic signs are an important element in maintaining smoothness and safety on the road. However, there are still many drivers who violate them, causing various negative impacts. Traffic Sign Recognition (TSR) is a technology that has the ability to detect and identify various types of traffic signs by utilizing artificial intelligence in the computer vision domain. TSR has been applied in various vehicle applications, such as Advance Driver Assistance System (ADAS) and Autonomous Driving System (ADS). This system is made by integrating camera sensors with the YOLOv8 algorithm which has high accuracy and fast data processing. The data used were 2093 images and annotated through Roboflow. Then the data is trained through Google Collaboratory with a mAP evaluation result of 95.5% which shows that the system can detect objects accurately. The level of precision and success of the model in detecting objects is 93.5% and the success is 93.3%. The results of system testing can use images, videos, and through cameras in real time.

PREDICTION OF CRUDE OIL PRICES IN INDONESIA USING FOURIER SERIES ESTIMATOR AND ARIMA METHOD

Crude oil is one of the non-renewable natural resources that is crucial for countries around the world in driving economic development. However, the availability of crude oil is decreasing over time. The high demand for crude oil results in scarcity which causes price fluctuations. Low oil prices can reduce state revenues, disrupt development programs, and even trigger budget deficits. On the other hand, an increase in crude oil prices can make a positive contribution to state revenues. Crude oil exports become more profitable, which can increase state revenue through royalties and taxes levied on the oil and gas sector. This additional revenue can be used to support infrastructure development, social programs, and investment in key sectors of the economy. High oil prices can also harm the economy. With the many impacts that can be caused by crude oil prices, the government must be able to anticipate and prepare for it. The data used in this study are data on crude oil prices in Indonesia for monthly periods from January 2018 to October 2023 sourced from the official website of the Ministry of Energy and Mineral Resources (ESDM) of the Republic of Indonesia. The researcher tried to compare two analysis methods, namely the Fourier series and the ARIMA estimator. The results of this study show that the best method in predicting crude oil prices in Indonesia is the Fourier series estimator with Cos-Sin function which produces RMSE and MAPE values of 7.93 and 8.4%. The prediction results can be used as a reference for the government to anticipate and make programs or policies that are more focused and targeted toward the impacts that can be caused by changes in crude oil prices.

Pendekatan Neural Network dalam Peramalan Jumlah Penduduk Kota Semarang dengan Menggunakan Metode Backpropagation

The city of Semarang is one of the metropolitan cities that has a fairly dense population. During the years 2010-2021 the City of Semarang has a population fluctuation. It is necessary to make predictions of population data in order to plan the development in the City of Semarang can be better planned and can regulate the fluctuation of population in the future. In this study, the results of the prediction of the population of the City of Semarang were analyzed using the Neural Network approach with the backpropagation method. After training and testing, the best architectural model was obtained with 2 neurons on the input layer, 2 neurones on the hidden layer and 1 neuron on the output layer. Based on the results of the best architectural model, the MSE score was 9.39749 x 10-6 and the average MAPE value was 0.884552461%. The evaluation result with the MAPE value is very accurate because it is < 10%. In this study, the results of the forecast of the population of the city of Semarang in 2022-2025 consecutive are 1,863.121 people, 1,878.634 people, 1.891.865 people, and 1,902.947 people.

Multivariate Imputation Chained Equation on Solar Radiation in Automatic Weather Station

Solar radiation is one of the crucial weather observation variables Its variable has a role in renewable energy solutions, agriculture, meteorology, and hydrology. AWS is one of instrument that use to observing weather especially solar radiation. AWS has a pyranometer sensor used to measure solar radiation. Unfortunately, the instrument has problem like the igh cost of supplying, installing, maintaining, and calibrating the equipment. Due to this, there is a lot of empty data, and the actual data cannot be properly measured. Imputation of solar radiation data using MICE algorithm can be solution. This study using BLR, NRR and RFR estimator to estimating solar radiation data. AWS Staklim Banten as target and other AWS as input. The period from January 1, 2018 - February 12, 2024. The performance evaluation of the solar radiation imputation estimator is still according to WMO operational requirements for solar radiation measurements, which can be seen from the resulting MAPE value < 8%.

Comparison of Nonparametric Regression Nadara - Watson Estimator Kernel Function And Local Polynomial Regression In Predicting USD Against IDR

Introduction: Macroeconomic problems such as inflation and exchange rates are often highlighted as benchmarks for achieving economic progress. The stability of both must be monitored by the government in order to control the inflation rate and exchange rate. This instability is a phenomenon of fluctuation, namely the phenomenon of the rise and fall of the exchange rate of a currency based on demand and supply. Given the large impact of exchange rate fluctuations on the economy, the prediction of the wage exchange rate against the US dollar is considered necessary because it is useful to anticipate and minimize bad possibilities that arise. Method: Methods that can be used to analyze fluctuating currency exchange rate data are nonparametric regression, Nadaraya-Watson estimator, Gaussian kernel function, and Local Polynomial Regression. Results and Discussion: The results of a nonparametric regression comparison between the Nadaraya-Watson estimator, Gaussian kernel function, and local polynomial regression were obtained by MAPE of 2.508% and 0.179%, respectively. This shows that the best model uses the local polynomial regression method and predicted USD exchange rate data against IDR using the best model, namely Local polynomial Regression where the MAPE value is less than 10%, which means the prediction rate is very good. Conclusion: The nonparametric regression method of the Nadaraya-Watrson estimator, Gaussian kernel function, and local polynomial regression shows that the best model uses the local polynomial regression method.

AI-driven design for the compressive strength of ultra-high performance geopolymer concrete (UHPGC): From explainable ensemble models to the graphical user interface

Ultra-high performance geopolymer concrete (UHPGC) has become of interest in recent years as a more economical and sustainable alternative while offering similar mechanical performance to ordinary ultra-high performance concrete (UHPC). The lack of an effective mix design methodology has inhibited the widespread use of UHPGC, despite its potential. This paper adopted an artificial intelligence (AI)-based approach to accurately model the compressive strength (CS) of UHPGC, a critical parameter to ensure structural integrity and reliability. Ensemble machine learning (ML) models such as RF, XGBoost, LightGBM and AdaBoost, which have been very popular lately, were selected as AI algorithms. For the establishment of these models, a comprehensive and reliable dataset of 181 test results was used, including 13 input features. Additionally, feature importance and Shapley additive explanations (SHAP) analyses were used to ensure the explainability of the prediction models and tackle the "black box" challenge of ML models. The results obtained revealed that all ensemble models successfully predicted the CS of UHPGC; in particular, the XGBoost model consistently exhibited the best overall performance in terms of higher R2 (0.948) and lower RMSE (6.68), MAE (4.73), MAPE (4 %), and mean error value (1.095), in the test phase. Moreover, feature importance and SHAP analyses revealed that the most influential features on the CS of UHPGC were age, fiber and silica fume, sodium silicate (Na2SiO3), and water content. Lastly, a graphical user interface (GUI) was developed to easily predict the CS of UHPGC in practical applications.

MODEL PERAMALAN PERGERAKAN PENUMPANG DOMESTIK DI BANDAR UDARA YOGYAKARTA INTERNATIONAL AIRPORT

This research is quantitative descriptive research which aims to make predictions using the ARIMA model time series forecasting method. The data obtained was analyzed using the R program. The data used for forecasting is the number of arrivals and departures of domestic passengers at Yogyakarta International Airport for the period January 2022 – December 2023. Based on the results of data analysis, it is known that the ARIMA model time series forecasting method is the best for predicting the number of arrivals. and domestic passenger departures at Yogyakarta International Airport are the AR (1,0,0) model. This method was chosen because the results were significant, met the white noise requirements and had the smallest MAPE value.

Computational investigation and biological activity of selected Schiff bases

Two Schiff bases, (E)-N-(4-bromophenyl)-1-(2,6-dichlorophenyl)methanimine (S1) and (E)-4-(((4-bromophenyl)imino)methyl)phenol (S2) were synthesized and elucidated using basic spectroscopic methods such as Fourier-transform-infrared, Ultraviolet-visible as well as 1H & 13C-NMR while purity of the compounds was ascertained by the means of elemental analysis. The stability, chemical reactivities and electronic properties of S1 and S2 were evaluated using Density Functional Theory (DFT). Energy band gap (?E) of S1 and S2 are 2.81 eV and 3.02 eV respectively and this inferred that S1 is more reactive as well as less stable than S2. The Molecular electrostatic potential (MEP) mapping evaluation showed that both S1 and S2 are having more of the yellow, green and red region than the blue region, signifying that these structures are prone to electrophilic attack. The compounds were screened against one Gram-negative (Staphylococcus aureus) and two Gram-positive bacteria (Klebsiella pneumoniae and Psedomonas aeruginosa) to evaluate their antibacterial potential. S1 as well as S2 showed antibacterial potential against Staphylococcus aureus and exhibited less of no activity against Klebsiella pneumoniae and Escherichia coli. In silico studies of S1 and S2 against Staphylococcus aureus was carried out and the outcomes corroborate with experimental findings. Pharmacological evaluation of S1 and S2 showed that both compounds exhibited less violation of Lipinski’s rule of five (Ro5) which pose them to be less toxic and orally available.

A Hybrid Traditional and Machine Learning-Based Stacking-Based Ensemble Forecasting Approach for Coal Price Prediction

Accurate coal price forecasts are crucial, as volatility in coal prices significantly impacts company performance and profitability. Traditional time series forecasting methods, such as exponential smoothing, are known for their simplicity and low data requirements. In contrast, machine learning techniques, such as random forest and neural network, offer higher accuracy in predictions. However, very few attempts have been made to combine the simplicity of traditional methods with the accuracy of machine learning techniques. This paper presents a novel stacking-based model that integrates both traditional statistical methods and machine learning techniques to enhance coal price predictions. Using Indonesian coal price data from January 2009 to October 2021, we trained the models on various combinations of predictors to generate new predictions. Our findings demonstrate that our stacking-based model outperforms other models, with RMSE and MAPE values of 6.44 and 5.97%, respectively. These results indicate that the model closely forecasts actual coal prices, capturing 94.03% of the price movements. The main contribution of this study is the application of stacking-based models to coal price forecasting in Indonesia, which has not been previously explored, thus enriching the literature on this topic.

Employing an Artificial Neural Network Model to Predict Citrus Yield Based on Climate Factors

Agricultural sustainability is dependent on the ability to predict crop yield, which is vital for farmers, consumers, and researchers. Most of the works used the amount of rainfall, average monthly temperature, relative humidity, etc. as inputs. In this paper, an attempt was made to predict the yield of the citrus crop (Washington Navel orange, Valencia orange, Murcott mandarin, Fremont mandarin, and Bearss Seedless lime) using weather factors and the accumulated heat units. These variables were used as input parameters in an artificial neural network (ANN) model. The necessary information was gathered during the growing seasons between 2010/2011 and 2021/2022 under Egyptian conditions. Weather factors were daily precipitation, yearly average air temperature, and yearly average of air relative humidity. A base air temperature of 13.0 °C was used to determine the accumulated heat units. The heat use efficiency (HUE) for cultivars was determined. The Bearss Seedless lime had the lowest HUE of 9.5 kg/ha °C day, while the Washington Navel orange had the highest HUE of 20.2 kg/ha °C day. The predictive performance of the ANN model with a structure of 9-20-1 with the backpropagation was evaluated using standard statistical measures. The actual and estimated yields from the ANN model were compared using a testing dataset, resulting in a value of RMSE, MAE, and MAPE of 2.80 t/ha, 2.58 t/ha, and 5.41%, respectively. The performance of the ANN model in the training phase was compared to multiple linear regression (MLR) models using values of R2; for MLR models for all cultivars, R2 ranged between 0.151 and 0.844, while the R2 value for the ANN was 0.87. Moreover, the ANN model gave the best performance criteria for evaluation of citrus yield prediction with a high R2, low root mean squared error, and low mean absolute error compared to the performance criteria of data mining algorithms such as K-nearest neighbor (KNN), KStar, and support vector regression. These encouraging outcomes show how the current ANN model can be used to estimate fruit yields, including citrus fruits and other types of fruit. The novelty of the proposed ANN model lies in the combination of weather parameters and accumulated heat units for accurate citrus yield prediction, specifically tailored for Egyptian regional citrus crops. Furthermore, especially in low- to middle-income countries such as Egypt, the findings of this study can greatly enhance the reliance on statistics when making decisions regarding agriculture and climate change. The citrus industry can benefit greatly from these discoveries, which can help with optimization, harvest planning, and postharvest logistics. We recommended furthering proving the robustness and generalization ability of the results in this study by adding more data points.

Joint Component Estimation for Electricity Price Forecasting Using Functional Models

This work considers the issue of modeling and forecasting electricity prices within the functional time series approach. As this is often performed by estimating and predicting the different components of the price dynamics, we study whether jointly modeling the components, able to account for their inter-relations, could improve prediction with respect to a separate instance of modeling. To investigate this issue, we consider and compare the predictive performance of four different predictors. The first two, namely Smoothing Splines-Seasonal Autoregressive (SS-SAR) and Smoothing Splines-Functional Autoregressive (SS-FAR) are based on separate modeling while the third one is derived from a single-step procedure that jointly estimates all the components by suitably including exogenous variables. It is called Functional Autoregressive with eXogenous variables (FARX) model. The fourth one is a combination of the SS-FAR and FARX predictors. The predictive performances of the models are tested using electricity price data from the northern zone of the Italian electricity market (IPEX), both in terms of forecasting error indicators (MAE, MAPE and RMSE) and by means of the Diebold and Mariano test. The results point out that jointly estimating the components leads to significantly more accurate predictions than using a separate instance of modeling. In particular, the MAE, MAPE, and RMSE values for the best predictor, based on the FARX(3,0,4) model, are 4.25, 9.28, and 5.38, respectively. The percentage error reduction is about 20% with respect to SS-SAR(3,1) and about 10% with respect to SS-FAR(5). Finally, this study suggests that the forecasting errors are generally higher on Sunday and Monday, from hours 3 to 6 in the morning and 14 to 15 in the afternoon, and in June and December. On the other hand, prices are relatively lower on Wednesday, Thursday, and Friday, from hour 20 to 1 a.m., and in January and February.

Fire resistance time prediction and optimization of cold-formed steel walls based on machine learning

Many full-scale experiments and numerical studies have been conducted to determine the fire performance of cold-formed steel (CFS) walls, but these studies are expensive and time consuming. This study proposes a machine learning (ML) based framework aiming at accurately predicting the fire resistance time (FRT) and optimizing the design of CFS walls under ISO 834 fire condition. To overcome the limitation of 32 experimental data points in the literature, a validated numerical method was used to generate 592 data points to expand the dataset of CFS walls, considering different wall configurations, various sheathing board types and thicknesses. The XGBoost (eXtreme Gradient Boosting) model was trained with numerical data and tested with experimental data. The hyperparameter tuning of the XGBoost model was implemented with Bayesian optimization, and it was found that the XGBoost model accurately predicted the FRT of CFS walls, with R2 and MAPE values being 0.933 and 8.46 %, respectively. The prediction process of the XGBoost model was interpreted by the SHAP (SHapley Additive exPlanations) method to determine the relative importance of input variables. The NSGA-II algorithm was adopted to optimize the FRT-cost dual-objective of CFS walls and the Pareto front including optimal solutions was emerging. The cost for each solution of Pareto front sets was lower than that in the real dataset at same FRT level. This phenomenon implied that the ML-based optimization framework successfully identified the cost-efficient design process. The proposed ML-based optimization framework offers a promising alternative for engineers to design CFS walls effectively with both mechanical and economic objectives.

Comparison of the Fuzzy Time Series Chen Model and the Heuristic Model in Forecasting the Number of International Tourists in West Sumatra

The Fuzzy Time Series Chen and Heuristic are two forecasting methods based on fuzzy logic used to predict values in time series. The FTS Chen and Heuristic models have almost identical forecasting processes, but the main difference lies in how they develop fuzzy logical relationships. The FTS Chen model uses Fuzzy Logical Relationship Groups obtained from the results of Fuzzy Logical Relationships for the forecasting process. On the other hand, the FTS Heuristic model uses Fuzzy Logical Relationships directly in the forecasting process. Fuzzy Logical Relationships are a collection of fuzzy logical relationships used to connect values in time series. By using Fuzzy Logical Relationships, the Heuristic model can predict values in time series more accurately and effectively. The forecasting is done to plan the development of tourism infrastructure, determine service needs, and optimize tourism promotion. The data shows that the number of foreign tourists visiting West Sumatra has continued to grow from 2006 to 2023. The comparison of the accuracy of the forecasting results of FTS Chen and Heuristic models for foreign tourists in West Sumatra yielded a MAPE of 0.241% for FTS model Chen and 0.194% for FTS model Heuristic. This indicates that the best forecasting model for foreign tourists is the Heuristic model due to its lower MAPE value.

Enhanced solar power prediction using CNN and ladybug beetle optimization algorithm

Integrating renewable energy sources like solar power into the grid necessitates accurate prediction methods to optimize their utilization. This paper proposes a novel approach that combines Convolutional Neural Networks (CNN) with the Ladybug Beetle Optimization (LBO) algorithm to forecast solar power generation efficiently. Many traditional models, for predicting power often struggle with accuracy and efficiency when it comes to computations. To overcome these challenges, we utilize the capabilities of CNN to extract features and recognize patterns from past irradiance data. The CNN structure is skilled at capturing relationships within the input data allowing it to detect patterns that are natural in solar irradiance changes. Additionally, we apply the LBO algorithm inspired by how ladybug beetles search for food to tune the parameters of the CNN model. LBO imitates how ladybug beetles explore to find solutions making it effective in adjusting the hyperparameters of the CNN. This research utilizes a dataset with solar irradiance readings to train and test the proposed CNN-LBO framework. The performance of this model is assessed using evaluation measures, like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), MAPE, and R2 value. The experimental outcomes indicate that our hybrid CNN-LBO method surpasses existing techniques in terms of efficiency.

Inventory Prediction Using a Modified Multi-Dimensional Collaborative Wrapped Bi-Directional Long Short-Term Memory Model

Inventory prediction is concerned with the forecasting of future demand for products in order to optimize inventory levels and supply chain management. The challenges include demand volatility, data quality, multi-dimensional interactions, lead time variability, seasonal trends, and dynamic pricing. Nevertheless, these models suffer from numerous shortcomings, and in this research, we propose a new model, MMCW-BiLSTM (modified multi-dimensional collaboratively wrapped BiLSTM), for inventory prediction. The MMCW-BiLSTM model reflects a considerable leap in inventory forecasting by combining a number of components in order to consider intricate temporal dependencies and incorporate feature interactions. The MMCW-BiLSTM makes use of BiLSTM layers, collaborative attention mechanisms, and a multi-dimensional attention approach to learn from augmented datasets consisting of the original features and the extracted time series data. Moreover, adding a Taylor series transformation allows for a more precise description of the features in the model, thus improving the prediction precision. The results show that the models make the least mistakes when they use the AV demand forecasting dataset, with MAE values of 1.75, MAPE values of 2.89, MSE values of 6.76, and RMSE values of 2.6. Similarly, when utilizing the product demand dataset, the model also achieves the lowest error values for these metrics at 1.97, 3.91, 8.76, and 2.96. Likewise, when utilizing the dairy goods sales dataset, the model also achieves the lowest error values for these metrics at 2.54, 3.69, 10.39, and 3.22.

Geotechnical Assessment of Selected Lateritic Soils in Southwest Nigeria for Road Construction and Development of Artificial Neural Network Mathematical Based Model for Prediction of the California Bearing Ratio

Investigation of the geotechnical characteristics of eighteen different lateritic soils within southwest Nigeria was carried out to determine their suitability for road construction. To achieve this goal, the lateritic soils samples were subjected to different laboratory tests, including specific gravity, Atterberg limits, grain size analysis, California bearing ratio, and compaction, in accordance with the ASTM standard procedure. The results of the tests showed that the specific gravity varied between 2.55 and 2.81; the linear shrinkage varied between 6.68% and 10.98%; the liquid limit varied between 37.17% and 56.93%; the plastic limit ranged from 19.47% to 37.14%; the plasticity index ranged from 3.81% to 30.29%; the fine sand content ranged from 37.07% to 62..93%; the fines content ranged from 36.4% to 60.9%; the maximum dry density ranged from 1747 kg/m3 to 2056 kg/m3 ; the optimum moisture content ranges from 10.94% to 20.51%; the un-soaked California bearing ratio ranged from 14.7% to 45.6%; and the soaked California bearing ratio ranged from 10% to 31%. Based on these results, all the studied soils can be used as road subgrade, while none except Loc.5/S1 is suitable for road subbase. However, none of the soils meets up with the requirement for road base course. The suitability of laterite for the construction of road depends largely on the California bearing ratio. However, laboratory tests for determining the California bearing ratio is tedious, time consuming and costly. As a result of this difficulty, there is a need to develop soft computing models to predict laterite California bearing ratio from index properties with cheap and simple tests. Thus, the experimental datasets of the eighteen studied lateritic soils were used to create and train artificial neural network (ANN) models to predict California bearing ratio from liquid limits, plasticity index, linear shrinkage, fine sand content and fines content. The proposed ANN models were compared with the multiple linear regression models proposed in this study and various regression based models suggested in the literature via statistical analyses. Based on the model comparison, the proposed ANN models outperformed the rest of the models; they presented the highest R 2 and the lowest RMSE, MAPE and MAE values. Thus, the ANN models are validated. To enhance the practical application of the proposed ANN models, they were transformed into simple mathematical equations, which gave the same predictions as the direct ANN models. Thus, they can be used for practical purposes.

Evaluation of large-scale deforestation susceptibility mapping in the mountainous region of the Himalayas: A case study of the Khangchendzonga Biosphere Reserve, India

The Khangchendzonga Biosphere Reserve (KBR) is located in the Eastern Himalayas and serves as a critical habitat for endemic species of flora and fauna, as well as playing a key role in carbon sequestration. The primary aim of this study was to map large-scale deforestation susceptibility zones in the mountainous region of KBR. The study area was divided into three zones: (a) Transition Zone, (b) Core Zone, and (c) Buffer Zone. This study utilized multiple remote sensing datasets acquired through the Google Earth Engine (GEE) platform, including precipitation, temperature, elevation, forest density, distance from rivers, NDVI, NDSI, distance from settlements, settlement density, distance from roads, and land use and land cover data. Additionally, the Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW) methods were employed to map deforestation susceptibility. To validate the proposed deforestation susceptibility, Hansen Global Forest Change (HGFC) data from 2001 to 2022 were used. Moreover, deforestation susceptibility was evaluated using the Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC) metrics. Notably, our findings revealed significant declines in tree cover of 1.60%, 1.27%, and 0.89% in the Transition, Core, and Buffer Zones, respectively, during critical years (2009, 2011, 2019, 2020). These periods witnessed substantial deforestation, indicating a deteriorating condition of the reserve's forest cover. Although there were minor discrepancies in the results of the two methods, both highlighted the particular vulnerability of the transition zones in the eastern and southern regions of KBR. The comprehensive methodology employed in this research establishes an advanced spatial data infrastructure that is indispensable for immediate conservation planning and adaptive management strategies. The insights gleaned from this investigation hold substantial promise for guiding future restoration and conservation efforts aimed at enriching biodiversity and fortifying ecosystem services in this critical area.

Multisite Evaluation and Validation of Optical Genome Mapping for Prenatal Genetic Testing

Prenatal diagnostic testing of amniotic fluid, chorionic villi, or more rarely, fetal cord blood is recommended following a positive or unreportable noninvasive cell-free fetal DNA test, abnormal maternal biochemical serum screen, abnormal ultrasound, or increased genetic risk for a cytogenomic abnormality based on family history. Although chromosomal microarray is recommended as the first-tier prenatal diagnostic test, in practice, multiple assays are often assessed in concert to achieve a final diagnostic result. The use of multiple methodologies is costly, time consuming, and labor intensive. Optical genome mapping (OGM) is an emerging technique with application for prenatal diagnosis because of its ability to detect and resolve, in a single assay, all classes of pathogenic cytogenomic aberrations. In an effort to characterize the potential of OGM as a novel alternative to traditional standard of care (SOC) testing of prenatal samples, OGM was performed on a total of 200 samples representing 123 unique cases, which were previously tested with SOC methods (92/123=74.7% cases tested with at least two SOCs). OGM demonstrated an overall accuracy of 99.6% when compared with SOC methods, a positive predictive value of 100%, and 100% reproducibility between sites, operators, and instruments. The standardized workflow, cost-effectiveness, and high-resolution cytogenomic analysis demonstrate the potential of OGM to serve as a first-tier test for prenatal diagnosis.