Artificial Intelligence-based Models Research Articles

A Food Intake Estimation System Using an Artificial Intelligence-Based Model for Estimating Leftover Hospital Liquid Food in Clinical Environments: Development and Validation Study.

Medical staff often conduct assessments, such as food intake and nutrient sufficiency ratios, to accurately evaluate patients' food consumption. However, visual estimations to measure food intake are difficult to perform with numerous patients. Hence, the clinical environment requires a simple and accurate method to measure dietary intake. This study aims to develop a food intake estimation system through an artificial intelligence (AI) model to estimate leftover food. The accuracy of the AI's estimation was compared with that of visual estimation for liquid foods served to hospitalized patients. The estimations were evaluated by a dietitian who looked at the food photo (image visual estimation) and visual measurement evaluation was carried out by a nurse who looked directly at the food (direct visual estimation) based on actual measurements. In total, 300 dishes of liquid food (100 dishes of thin rice gruel, 100 of vegetable soup, 31 of fermented milk, and 18, 12, 13, and 26 of peach, grape, orange, and mixed juices, respectively) were used. The root-mean-square error (RMSE) and coefficient of determination (R2) were used as metrics to determine the accuracy of the evaluation process. Corresponding t tests and Spearman rank correlation coefficients were used to verify the accuracy of the measurements by each estimation method with the weighing method. The RMSE obtained by the AI estimation approach was 8.12 for energy. This tended to be smaller and larger than that obtained by the image visual estimation approach (8.49) and direct visual estimation approach (4.34), respectively. In addition, the R2 value for the AI estimation tended to be larger and smaller than the image and direct visual estimations, respectively. There was no difference between the AI estimation (mean 71.7, SD 23.9 kcal, P=.82) and actual values with the weighing method. However, the mean nutrient intake from the image visual estimation (mean 75.5, SD 23.2 kcal, P<.001) and direct visual estimation (mean 73.1, SD 26.4 kcal, P=.007) were significantly different from the actual values. Spearman rank correlation coefficients were high for energy (ρ=0.89-0.97), protein (ρ=0.94-0.97), fat (ρ=0.91-0.94), and carbohydrate (ρ=0.89-0.97). The measurement from the food intake estimation system by an AI-based model to estimate leftover liquid food intake in patients showed a high correlation with the actual values with the weighing method. Furthermore, it also showed a higher accuracy than the image visual estimation. The errors of the AI estimation method were within the acceptable range of the weighing method, which indicated that the AI-based food intake estimation system could be applied in clinical environments. However, its lower accuracy than that of direct visual estimation was still an issue.

Sustainable cost-effective chemically modified banana leaf powder for methyl blue dye removal: kinetics, isotherm, thermodynamics and artificial intelligence-based analysis

This study investigates cellulose-based adsorbents that are naturally occurring and have a unique, abundant supply for adsorbing organic dye. The effectiveness of chemically modified banana leaf powder (CMBLP) as a biodegradable and environmentally friendly adsorbent for extracting methyl blue (MB) dye from aqueous solutions was studied. Point-zero charge (pHpzc) analysis, scanning electron microscopy (SEM), X-ray energy dispersion (EDS), and Fourier transform infrared spectroscopy (FTIR) were used to characterize the CMBLP. At pH 6.0, 100 mg/L of dye, 50 min of contact time, and 300 K temperature, the maximum removal values were obtained. Under ideal circumstances, CMBLP displays 77.13215% removal rate. Langmuir and Freundlich models, along with pseudo-first- and pseudo-second-order kinetics equations, were used to investigate the equilibrium isotherm and kinetics of CMBLP. The acquired experimental data fit pseudo-second-order kinetics (R2 = 0.9986 and 0.9984) and the Langmuir isotherm (R2 = 0.9996) better. Utilizing the Langmuir isotherm model, the maximum adsorption capacity (qmax) of CMBLP has been identified to be 118.24 mg/g. At a temperature of 300 K, the adsorbent was also able to adsorb the maximum amount of methyl blue (MB). The thermal properties of methyl blue (MB) dye adsorption validate the spontaneous and exothermic nature of the adsorption process. The majority of statistical and artificial intelligence-based model (like KNN, SVM, ANN and LSTM) validate the superiority of MB removal onto CMBLP and support our experimental findings.Graphical

P067 An interim analysis of the multi center retrospective study PROVIDENCE: Artificial intelligence-based models for estimating the risk of biochemical recurrence with clinical data

P067 An interim analysis of the multi center retrospective study PROVIDENCE: Artificial intelligence-based models for estimating the risk of biochemical recurrence with clinical data

Assessing forecast performance of daily reference evapotranspiration: A comparison of equations, machine and deep learning using weather forecasts

An accurate forecast of short-term reference evapotranspiration (ET0) is crucial for effective farm and irrigation scheduling management. The techniques of forecasting ET0 have progressed from empirical equations to artificial intelligence-based models. However, gaps have remained in comprehensively evaluating the ET0 forecast performance discrepancies between various methods based on weather forecasts in addition to historical meteorological data, as well as investigating the effect of the amount of input variables on ET0 forecast accuracy. Here, the study evaluated the forecast accuracy of five conventional equations calibrated and six machine learning (ML) and deep learning (DL) models in predicting daily ET0 for a lead time of 1–7 days in the North China Plain (NCP). Comparative evaluations indicate that the Analytical Penman-Monteith (APM) is the optimum empirical equation for forecasting ET0, but its accuracy decreases as lead times increase. Conversely, ML and DL models consistently outperform empirical equations, characterized by lower mean absolute error (MAE) and root mean square error (RMSE) as well as correlation coefficient and Nash-Sutcliffe efficiency coefficient approaching 1 across all lead times. Notably, the Bidirectional Long short-term memory (Bi-LSTM) model outperforms other models, maintaining robust and effective forecast performance even on day 7. Moreover, with the increasing input variables, combining weather forecasts and historical meteorological data, the forecast performance of all models is improved, with significant enhancement observed in the Bi-LSTM model. This work provides valuable insights for choosing appropriate models to forecast ET0, holding essential in regional managing farm and irrigation systems.

Improved survival prediction for kidney transplant outcomes using artificial intelligence-based models: development of the UK Deceased Donor Kidney Transplant Outcome Prediction (UK-DTOP) Tool

The UK Deceased Donor Kidney Transplant Outcome Prediction (UK-DTOP) Tool, developed using advanced artificial intelligence (AI), significantly enhances the prediction of outcomes for deceased-donor kidney transplants in the UK. This study analyzed data from the UK Transplant Registry (UKTR), including 29,713 transplant cases between 2008 and 2022, to assess the predictive performance of three machine learning models: XGBoost, Random Survival Forest, and Optimal Decision Tree. Among these, XGBoost demonstrated exceptional performance with the highest concordance index of 0.74 and an area under the curve (AUC) consistently above 0.73, indicating robust discriminative ability and calibration. In comparison to the traditional Kidney Donor Risk Index (KDRI), which achieved a lower concordance index of 0.57, the UK-DTOP model marked a significant improvement, underscoring its superior predictive accuracy. The advanced capabilities of the XGBoost model were further highlighted through calibration assessments using the Integrated Brier Score (IBS), showing a score of 0.14, indicative of precise survival probability predictions. Additionally, unsupervised learning via k-means clustering was employed to identify five distinct clusters based on donor and transplant characteristics, uncovering nuanced insights into graft survival outcomes. These clusters were further analyzed using Bayesian Cox regression, which confirmed significant survival outcome variations across the clusters, thereby validating the model’s effectiveness in enhancing risk stratification. The UK-DTOP tool offers a comprehensive decision-support system that significantly refines pre-transplant decision-making. The study’s findings advocate for the adoption of AI-enhanced tools in healthcare systems to optimize organ matching and transplant success, potentially guiding future developments in global transplant practices.

One novel transfer learning-based CLIP model combined with self-attention mechanism for differentiating the tumor-stroma ratio in pancreatic ductal adenocarcinoma.

To develop a contrastive language-image pretraining (CLIP) model based on transfer learning and combined with self-attention mechanism to predict the tumor-stroma ratio (TSR) in pancreatic ductal adenocarcinoma on preoperative enhanced CT images, in order to understand the biological characteristics of tumors for risk stratification and guiding feature fusion during artificial intelligence-based model representation. This retrospective study collected a total of 207 PDAC patients from three hospitals. TSR assessments were performed on surgical specimens by pathologists and divided into high TSR and low TSR groups. This study developed one novel CLIP-adapter model that integrates the CLIP paradigm with a self-attention mechanism for better utilizing features from multi-phase imaging, thereby enhancing the accuracy and reliability of tumor-stroma ratio predictions. Additionally, clinical variables, traditional radiomics model and deep learning models (ResNet50, ResNet101, ViT_Base_32, ViT_Base_16) were constructed for comparison. The models showed significant efficacy in predicting TSR in PDAC. The performance of the CLIP-adapter model based on multi-phase feature fusion was superior to that based on any single phase (arterial or venous phase). The CLIP-adapter model outperformed traditional radiomics models and deep learning models, with CLIP-adapter_ViT_Base_32 performing the best, achieving the highest AUC (0.978) and accuracy (0.921) in the test set. Kaplan-Meier survival analysis showed longer overall survival in patients with low TSR compared to those with high TSR. The CLIP-adapter model designed in this study provides a safe and accurate method for predicting the TSR in PDAC. The feature fusion module based on multi-modal (image and text) and multi-phase (arterial and venous phase) significantly improves model performance.

Performance of a Protein Language Model for Variant Annotation in Cardiac Disease.

Genetic testing is a cornerstone in the assessment of many cardiac diseases. However, variants are frequently classified as variants of unknown significance, limiting the utility of testing. Recently, the DeepMind group (Google) developed AlphaMissense, a unique artificial intelligence-based model, based on language model principles, for the prediction of missense variant pathogenicity. We aimed to report on the performance of AlphaMissense, accessed by VarCardio, an open web-based variant annotation engine, in a real-world cardiovascular genetics center. All genetic variants from an inherited arrhythmia program were examined using AlphaMissense via VarCard.io and compared with the ClinVar variant classification system, as well as another variant classification platform (Franklin by Genoox). The mutation reclassification rate and genotype-phenotype concordance were examined for all variants in the study. We included 266 patients with heritable cardiac diseases, harboring 339 missense variants. Of those, 230 (67.8%) were classified by ClinVar as either variants of unknown significance or nonclassified. Using VarCard.io, 198 variants of unknown significance (86.1%, 95% CI, 80.9-90.3) were reclassified to either likely pathogenic or likely benign. The reclassification rate was significantly higher for VarCard.io than for Franklin (86.1% versus 34.8%, P<0.001). Genotype-phenotype concordance was highly aligned using VarCard.io predictions, at 95.9% (95% CI, 92.8-97.9) concordance rate. For 109 variants classified as pathogenic, likely pathogenic, benign, or likely benign by ClinVar, concordance with VarCard.io was high (90.5%). AlphaMissense, accessed via VarCard.io, may be a highly efficient tool for cardiac genetic variant interpretation. The engine's notable performance in assessing variants that are classified as variants of unknown significance in ClinVar demonstrates its potential to enhance cardiac genetic testing.

The effect of Plantago major seed mucilage fortified with cell-free supernatant (CFS) of probiotic Lactococcus lactis NJ414 on extending shelf life and ensuring the safety of lamb meat slices stored at 4 °C

The present study was conducted to create a bioactive edible coating using Plantago major mucilage (PMM) and the cell-free supernatant (CFS) of probiotic Lactococcus lactis NJ414 to extend the shelf life and ensure the safety of lamb meat slices stored at 4 °C. The PMM+2%CFS coated slices showed a significant decrease in total viable count (8.14–5.48 log CFU/g), and psychrotrophic bacteria count (5.24–3.54 log CFU/g). Additionally, the pH value decreased from 5.93 to 5.71, the thiobarbituric acid (TBA) reduced by 65%, and the peroxide value (PV) decreased by 60%. Moreover, PMM+2%CFS coated slices had higher hardness (68.87 N vs. 62.10 N), moisture content (67.57% vs. 64.35%), and overall acceptance (6.40 vs. 3.00) compared to the control. Adaptive neuro-fuzzy inference systems (ANFIS) and gaussian process regression (GPR) models were used to predict the population of microbial pathogens, physicochemical and sensory characteristics of coated lamb products based on three inputs including PMM (0% and 2%), CFS (0%, 1% and 2%), time (1, 3, 6 and 9 days). The result showed that artificial intelligence-based models, especially ANFIS (R2 > 0.99) are able to estimate the population of microbial pathogens, physiochemical and sensory characteristics.

Multi-modality artificial intelligence-based transthyretin amyloid cardiomyopathy detection in patients with severe aortic stenosis.

Transthyretin amyloid cardiomyopathy (ATTR-CM) is a frequent concomitant condition in patients with severe aortic stenosis (AS), yet it often remains undetected. This study aims to comprehensively evaluate artificial intelligence-based models developed based on preprocedural and routinely collected data to detect ATTR-CM in patients with severe AS planned for transcatheter aortic valve implantation (TAVI). In this prospective, single-center study, consecutive patients with AS were screened with [99mTc]-3,3-diphosphono-1,2-propanodicarboxylic acid ([99mTc]-DPD) for the presence of ATTR-CM. Clinical, laboratory, electrocardiogram, echocardiography, invasive measurements, 4-dimensional cardiac CT (4D-CCT) strain data, and CT-radiomic features were used for machine learning modeling of ATTR-CM detection and for outcome prediction. Feature selection and classifier algorithms were applied in single- and multi-modality classification scenarios. We split the dataset into training (70%) and testing (30%) samples. Performance was assessed using various metrics across 100 random seeds. Out of 263 patients with severe AS (57% males, age 83 ± 4.6years) enrolled, ATTR-CM was confirmed in 27 (10.3%). The lowest performances for detection of concomitant ATTR-CM were observed in invasive measurements and ECG data with area under the curve (AUC) < 0.68. Individual clinical, laboratory, interventional imaging, and CT-radiomics-based features showed moderate performances (AUC 0.70-0.76, sensitivity 0.79-0.82, specificity 0.63-0.72), echocardiography demonstrated good performance (AUC 0.79, sensitivity 0.80, specificity 0.78), and 4D-CT-strain showed the highest performance (AUC 0.85, sensitivity 0.90, specificity 0.74). The multi-modality model (AUC 0.84, sensitivity 0.87, specificity 0.76) did not outperform the model performance based on 4D-CT-strain only data (p-value > 0.05). The multi-modality model adequately discriminated low and high-risk individuals for all-cause mortality at a mean follow-up of 13 months. Artificial intelligence-based models using collected pre-TAVI evaluation data can effectively detect ATTR-CM in patients with severe AS, offering an alternative diagnostic strategy to scintigraphy and myocardial biopsy.

An artificial intelligence-based model exploiting H&E images to predict recurrence in negative sentinel lymph-node melanoma patients

BackgroundRisk stratification and treatment benefit prediction models are urgent to improve negative sentinel lymph node (SLN-) melanoma patient selection, thus avoiding costly and toxic treatments in patients at low risk of recurrence. To this end, the application of artificial intelligence (AI) could help clinicians to better calculate the recurrence risk and choose whether to perform adjuvant therapy.MethodsWe made use of AI to predict recurrence-free status (RFS) within 2-years from diagnosis in 94 SLN- melanoma patients. In detail, we detected quantitative imaging information from H&E slides of a cohort of 71 SLN- melanoma patients, who registered at Istituto Tumori “Giovanni Paolo II” in Bari, Italy (investigational cohort, IC). For each slide, two expert pathologists firstly annotated two Regions of Interest (ROIs) containing tumor cells alone (TUMOR ROI) or with infiltrating cells (TUMOR + INF ROI). In correspondence of the two kinds of ROIs, two AI-based models were developed to extract information directly from the tiles in which each ROI was automatically divided. This information was then used to predict RFS. Performances of the models were computed according to a 5-fold cross validation scheme. We further validated the prediction power of the two models on an independent external validation cohort of 23 SLN- melanoma patients (validation cohort, VC).ResultsThe TUMOR ROIs have revealed more informative than the TUMOR + INF ROIs. An Area Under the Curve (AUC) value of 79.1% and 62.3%, a sensitivity value of 81.2% and 76.9%, a specificity value of 70.0% and 43.3%, an accuracy value of 73.2% and 53.4%, were achieved on the TUMOR and TUMOR + INF ROIs extracted for the IC cohort, respectively. An AUC value of 76.5% and 65.2%, a sensitivity value of 66.7% and 41.6%, a specificity value of 70.0% and 55.9%, an accuracy value of 70.0% and 56.5%, were achieved on the TUMOR and TUMOR + INF ROIs extracted for the VC cohort, respectively.ConclusionsOur approach represents a first effort to develop a non-invasive prognostic method to better define the recurrence risk and improve the management of SLN- melanoma patients.

Validation of an Artificial Intelligence-Based Model for Early Childhood Caries Detection in Dental Photographs.

Background/Objectives: Early childhood caries (ECC) is a widespread and severe oral health problem that potentially affects the general health of children. Visual-tactile examination remains the diagnostic method of choice to diagnose ECC, although visual examination could be automated by artificial intelligence (AI) tools in the future. The aim of this study was the external validation of a recently published and freely accessible AI-based model for detecting ECC and classifying carious lesions in dental photographs. Methods: A total of 143 anonymised photographs of anterior deciduous teeth (ECC = 107, controls = 36) were visually evaluated by the dental study group (reference test) and analysed using the AI-based model (test method). Diagnostic performance was determined statistically. Results: ECC detection accuracy was 97.2%. Diagnostic performance varied between carious lesion classes (noncavitated lesions, greyish translucency/microcavity, cavitation, destructed tooth), with accuracies ranging from 88.9% to 98.1%, sensitivities ranging from 68.8% to 98.5% and specificities ranging from 86.1% to 99.4%. The area under the curve ranged from 0.834 to 0.964. Conclusions: The performance of the AI-based model is similar to that reported for the internal dataset used by developers. Further studies with independent image samples are required to comprehensively gauge the performance of the model.

300.5: Improved survival prediction for kidney transplant outcome prediction using artificial intelligence-based models: Development of a UK deceased donor kidney transplant outcome prediction (UK-DTOP) Tool.

300.5: Improved survival prediction for kidney transplant outcome prediction using artificial intelligence-based models: Development of a UK deceased donor kidney transplant outcome prediction (UK-DTOP) Tool.

Misclassification of Eyes With Progressive Keratoconus Using the KISA% Index.

To determine the misclassification rate of the keratoconus percentage (KISA%) index efficacy in eyes with progressive keratoconus. This was a retrospective case-control study of consecutive patients with confirmed progressive keratoconus and a contemporaneous normal control group with 1.00 diopters or greater regular astigmatism. Scheimpflug imaging (Pentacam HR) was obtained for all patients. KISA% index and inferior-superior (IS) values were obtained from the Pentacam topometric/keratoconus staging map. Receiver operating characteristic curves were generated to determine the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity values. There were 160 eyes from 160 patients evaluated, including 80 eyes from 80 patients with progressive keratoconus and 80 eyes from 80 control patients. There were 20 eyes (25%) with progressive keratoconus misclassified by the KISA% index, with 16 eyes (20%) of the progressive keratoconus cohort classified as normal (ie, KISA% < 60). There were 4 eyes (5%) with progressive keratoconus that would classify as having "normal topography" using the published criteria for very asymmetric ectasia with normal topography of KISA% less than 60 and IS value less than 1.45. All controls had a KISA% index value of less than 15. The optimal cut-off value to distinguish cohorts was 15.31 (AUROC = 0.972, 93.75% sensitivity). KISA% index values of 60 and 100 achieved low sensitivity (80% and 73.75%, respectively). The KISA% index misclassified a significant proportion of eyes with progressive keratoconus as normal. Although highly specific for clinical keratoconus, the KISA% index lacks sensitivity, does not effectively discriminate between normal and abnormal topography, and thus should not be used in large data analysis or artificial intelligence-based modeling. [J Refract Surg. 2024;40(9):e614-e624.].

Safeguards-related event detection in surveillance video using semi-supervised learning approach

We develop a deep learning model employing a semi-supervised learning approach, which can detect automatically safeguards-related events in nuclear facility from surveillance video. Our model is designed after a comprehensive analysis of the trends in artificial intelligence-based models to identify abnormal events in video. Our model incorporates a reconstruction module and a prediction module independently. The reconstruction module is trained to generate video frames within a sliding window, while the prediction module is trained to predict future motion feature based on the motion features within the video frames in a sliding window. Each module utilizes an autoencoder with a memory module positioned between an encoder and an decoder of the autoencoder. We evaluate the model's performance using a benchmark dataset and a self-produced dataset obtained from facility related to pyroprocessing. Our model's performanace is comparable to or superior to that of the prevous models from the benchmark dataset analysis, and all the abnormal events can be detected without false positive error from the self-produced dataset analysis.

Artificial Intelligence-Based Models for Prediction of Mortality in ICU Patients: A Scoping Review.

Healthcare professionals may be able to anticipate more accurately a patient's timing of death and assess their possibility of recovery by implementing a real-time clinical decision support system. Using such a tool, the healthcare system can better understand a patient's condition and make more informed judgements about distributing limited resources. This scoping review aimed to analyze various death prediction AI (Artificial Intelligence) algorithms that have been used in ICU (Intensive Care Unit) patient populations. The search strategy of this study involved keyword combinations of outcome and patient setting such as mortality, survival, ICU, terminal care. These terms were used to perform database searches in MEDLINE, Embase, and PubMed up to July 2022. The variables, characteristics, and performance of the identified predictive models were summarized. The accuracy of the models was compared using their Area Under the Curve (AUC) values. Databases search yielded an initial pool of 8271 articles. A two-step screening process was then applied: first, titles and abstracts were reviewed for relevance, reducing the pool to 429 articles. Next, a full-text review was conducted, further narrowing down the selection to 400 key studies. Out of 400 studies on different tools or models for prediction of mortality in ICUs, 16 papers focused on AI-based models which were ultimately included in this study that have deployed different AI-based and machine learning models to make a prediction about negative patient outcome. The accuracy and performance of the different models varied depending on the patient populations and medical conditions. It was found that AI models compared with traditional tools like SAP3 or APACHE IV score were more accurate in death prediction, with some models achieving an AUC of up to 92.9%. The overall mortality rate ranged from 5% to more than 60% in different studies. We found that AI-based models exhibit varying performance across different patient populations. To enhance the accuracy of mortality prediction, we recommend customizing models for specific patient groups and medical contexts. By doing so, healthcare professionals may more effectively assess mortality risk and tailor treatments accordingly. Additionally, incorporating additional variables-such as genetic information-into new models can further improve their accuracy.

Modelling bridge deterioration using long short-term memory neural networks: a deep learning-based approach

PurposeBridge deterioration is a critical risk to public safety, which mandates regular inspection and maintenance to ensure sustainable transport services. Many models have been developed to aid in understanding deterioration patterns and in planning maintenance actions and fund allocation. This study aims at developing a deep-learning model to predict the deterioration of concrete bridge decks.Design/methodology/approachThree long short-term memory (LSTM) models are formulated to predict the condition rating of bridge decks, namely vanilla LSTM (vLSTM), stacked LSTM (sLSTM), and convolutional neural networks combined with LSTM (CNN-LSTM). The models are developed by utilising the National Bridge Inventory (NBI) datasets spanning from 2001 to 2019 to predict the deck condition ratings in 2021.FindingsResults reveal that all three models have accuracies of 90% and above, with mean squared errors (MSE) between 0.81 and 0.103. Moreover, CNN-LSTM has the best performance, achieving an accuracy of 93%, coefficient of correlation of 0.91, R2 value of 0.83, and MSE of 0.081.Research limitations/implicationsThe study used the NBI bridge inventory databases to develop the bridge deterioration models. Future studies can extend the model to other bridge databases and other applications in the construction industry.Originality/valueThis study provides a detailed and extensive data cleansing process to address the shortcomings in the NBI database. This research presents a framework for implementing artificial intelligence-based models to enhance maintenance planning and a guideline for utilising the NBI or other bridge inventory databases to develop accurate bridge deterioration models. Future studies can extend the model to other bridge databases and other applications in the construction industry.

Skin cancer classification based on an optimized convolutional neural network and multicriteria decision-making

Skin cancer is a type of cancer disease in which abnormal alterations in skin characteristics can be detected. It can be treated if it is detected early. Many artificial intelligence-based models have been developed for skin cancer detection and classification. Considering the development of numerous models according to various scenarios and selecting the optimum model was rarely considered in previous works. This study aimed to develop various models for skin cancer classification and select the optimum model. Convolutional neural networks (CNNs) in the form of AlexNet, Inception V3, MobileNet V2, and ResNet 50 were used for feature extraction. Feature reduction was carried out using two algorithms of the grey wolf optimizer (GWO) in addition to using the original features. Skin cancer images were classified into four classes based on six machine learning (ML) classifiers. As a result, 51 models were developed with different combinations of CNN algorithms, without GWO algorithms, with two GWO algorithms, and with six ML classifiers. To select the optimum model with the best results, the multicriteria decision-making approach was utilized to rank the alternatives by perimeter similarity (RAPS). Model training and testing were conducted using the International Skin Imaging Collaboration (ISIC) 2017 dataset. Based on nine evaluation metrics and according to the RAPS method, the AlexNet algorithm with a classical GWO yielded the optimum model, achieving a classification accuracy of 94.5%. This work presents the first study on benchmarking skin cancer classification with many models. Feature reduction not only reduces the time spent on training but also improves classification accuracy. The RAPS method has proven its robustness in the problem of selecting the best model for skin cancer classification.

Bee-inspired insights: Unleashing the potential of artificial bee colony optimized hybrid neural networks for enhanced groundwater level time series prediction.

Analysis of the change in groundwater used as a drinking and irrigation water source is of critical importance in terms of monitoring aquifers, planning water resources, energy production, combating climate change, and agricultural production. Therefore, it is necessary to model groundwater level (GWL) fluctuations to monitor and predict groundwater storage. Artificial intelligence-based models in water resource management have become prevalent due to their proven success in hydrological studies. This study proposed a hybrid model that combines the artificial neural network (ANN) and the artificial bee colony optimization (ABC) algorithm, along with the ensemble empirical mode decomposition (EEMD) and the local mean decomposition (LMD) techniques, to model groundwater levels in Erzurum province, Türkiye. GWL estimation results were evaluated with mean square error (MSE), coefficient of determination (R2), and residual sum of squares (RSS) and visually with violin, scatter, and time series plot. The study results indicated that the EEMD-ABC-ANN hybrid model was superior to other models in estimating GWL, with R2 values ranging from 0.91 to 0.99 and MSE values ranging from 0.004 to 0.07. It has also been revealed that promising GWL predictions can be made with previous GWL data.

Pursuing the elusive footsteps of malaria in peripheral blood smears utilizing artificial intelligence.

For over a century, the need to identify malaria in the peripheral blood has been the driving force behind the development of fundamental clinical microscopy techniques. In the study by Moysis etal., artificial intelligence-based model was utilized to identify and provide quantitative morphological characteristics of red blood cells typical to severe malaria anaemia, irrespective to the actual presence of visible parasites. Commentary on: Moysis etal. Leveraging deep learning for detecting red blood cell morphological changes in blood films from children with severe malaria anaemia. Br J Haematol 2024;205:699-710.

P-695 Optimizing oocyte donor stimulation: A revelation from 2,666 cases emphasizing the sole efficacy of exogenous FSH

Abstract Study question Does the administration of human menopausal gonadotrophin in controlled ovarian stimulation improve clinical outcomes in the oocyte donation program? Summary answer The addition of hMG (human menopausal gonadotropin) reduces the number of retrieved oocytes without improving embryo development or clinical outcomes in oocyte donation program. What is known already In controlled ovarian stimulation for assisted reproduction treatments, follicle stimulating hormone(FSH) is essential, but the importance of LH supplementation from menopausal origin is controversial. It is a common practice to use hMG to provide the LH effect. The standard preparation contains FSH and LH effects in a 1:1 ratio.The FSH component would recruit ovarian follicles and stimulate their growth, while the LH component would facilitate their maturation. Our aim was to compare the outcomes of intracytoplasmic sperm injection (ICSI) treatments using a gonadotropin-releasing hormone GnRH) antagonist protocol with recombinant FSH(FSHr) alone and supplemented with hMG in the oocyte donation program. Study design, size, duration This is a retrospective cohort study of 2,666 patients enrolled in the oocyte donation program at a single center over four consecutive years. Donor stimulation commenced with daily FSHr injections. The FSHr group (n = 2,078) continued FSHr, while the FSHr+hMG group (n = 588) received hMG. Participants/materials, setting, methods Donors received GnRH agonist via i.m. injection until a mean diameter &amp;gt;18 mm in at least eight follicles. Transvaginal oocyte retrieval was scheduled 36h later, followed by ICSI for all retrieved oocytes. Embryos were cultured in time-lapse systems and evaluated using an artificial intelligence-based model (iDAScore v2). Recipients underwent hormone replacement therapy for endometrial preparation. Our comparative study examined oocytes retrieved, embryo score, implantation, and live birth outcomes in fresh and frozen embryo transfers (ETs). Main results and the role of chance The number of retrieved oocytes was higher in the FSHr group than in the FSHr+hMG group: 24.9±10.6 vs. 18.2±8.8 (p &amp;lt; 0.001). In addition, embryos from the FSHr+hMG group exhibited slower early development, with significantly lower division times to two, three, four and five cells in the FSHr group (p &amp;lt; 0.005). However, late parameters of embryo development showed no differences. Automatic scoring by the deep learning algorithm was identical for blastocysts in the FSHr (4.8±2.8) and FSHr+hMG groups (4.8±2.8); p = 1. Implantation rates showed no significant differences for fresh ET (FSHr: 58.6% vs. FSHr+hMG: 61.9%; n = 2,073; p = 1) or frozen ET (FSHr: 49.3% vs. FSHr+hMG: 46%; n = 2,687; p = 1). Similarly, there were no differences in live birth rates for fresh ET (FSHr: 45.6% vs. FSHr+hMG: 50.3%; n = 1,993; p = 1) or frozen ET (FSHr: 31.8% vs. FSHr+hMG: 33.8%; n = 2,508; p = 1). Limitations, reasons for caution The retrospective and single-center nature of the study would be its main limitations. It is essential to emphasize that these results are only applicable within the oocyte donation program. Wider implications of the findings Based on our findings, the use of exogenous FSHr alone is likely adequate in the GnRH antagonist protocol for optimal ovarian stimulation of donors, leading to the production of high-quality embryos and successful clinical outcomes. Trial registration number Not Applicable