Test Set Research Articles

Optimizing predictions: improved performance of preoperative gadobenate-enhanced MRI hepatobiliary phase features in predicting vessels encapsulating tumor clusters in hepatocellular carcinoma-a multicenter study.

Vessels Encapsulating Tumor Clusters (VETC) are now recognized as independent indicators of recurrence and overall survival in hepatocellular carcinoma (HCC) patients. However, there has been limited investigation into predicting the VETC pattern using hepatobiliary phase (HBP) features from preoperative gadobenate-enhanced MRI. This study involved 252 HCC patients with confirmed VETC status from three different hospitals (Hospital 1: training set with 142 patients; Hospital 2: test set with 64 patients; Hospital 3: validation set with 46 patients). Independent predictive factors for VETC status were determined through univariate and multivariate logistic analyses. Subsequently, these factors were used to construct two distinct VETC prediction models. Model 1 included all independent predictive factors, while Model 2 excluded HBP features. The performance of both models was assessed using the Area Under the Curve (AUC), Decision Curve Analysis, and Calibration Curve. Prediction accuracy between the two models was compared using Net Reclassification Improvement (NRI) and Integrated Discriminant Improvement (IDI). CA199, IBIL, shape, peritumoral hyperintensity on HBP, and arterial peritumoral enhancement were independent predictors of VETC. Model 1 showed robust predictive performance, with AUCs of 0.836 (training), 0.811 (test), and 0.802 (validation). Model 2 exhibited moderate performance, with AUCs of 0.813, 0.773, and 0.783 in the respective sets. Calibration and decision curves for both models indicated consistent predictions between predicted and actual VETC, benefiting HCC patients. NRI showed Model 1 increased by 0.326, 0.389, and 0.478 in the training, test, and validation sets compared to Model 2. IDI indicated Model 1 increased by 0.036, 0.028, and 0.025 in the training, test, and validation sets compared to Model 2. HBP features from preoperative gadobenate-enhanced MRI can enhance the predictive performance of VETC in HCC.

Predicting Outcomes Following Lower Extremity Endovascular Revascularization Using Machine Learning.

Lower extremity endovascular revascularization for peripheral artery disease carries nonnegligible perioperative risks; however, outcome prediction tools remain limited. Using machine learning, we developed automated algorithms that predict 30-day outcomes following lower extremity endovascular revascularization. The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent lower extremity endovascular revascularization (angioplasty, stent, or atherectomy) for peripheral artery disease between 2011 and 2021. Input features included 38 preoperative demographic/clinical variables. The primary outcome was 30-day postprocedural major adverse limb event (composite of major reintervention, untreated loss of patency, or major amputation) or death. Data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, 6 machine learning models were trained using preoperative features. The primary model evaluation metric was area under the receiver operating characteristic curve. Overall, 21 886 patients were included, and 30-day major adverse limb event/death occurred in 1964 (9.0%) individuals. The best performing model for predicting 30-day major adverse limb event/death was extreme gradient boosting, achieving an area under the receiver operating characteristic curve of 0.93 (95% CI, 0.92-0.94). In comparison, logistic regression had an area under the receiver operating characteristic curve of 0.72 (95% CI, 0.70-0.74). The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.09. The top 3 predictive features in our algorithm were (1) chronic limb-threatening ischemia, (2) tibial intervention, and (3) congestive heart failure. Our machine learning models accurately predict 30-day outcomes following lower extremity endovascular revascularization using preoperative data with good discrimination and calibration. Prospective validation is warranted to assess for generalizability and external validity.

On the Validation of Protein Force Fields Based on Structural Criteria.

Molecular dynamics simulations depend critically on the quality of the force field used to describe the interatomic interactions and the extent to which it has been validated for use in a specific application. Using a curated test set of 52 high-resolution structures, 39 derived from X-ray diffraction and 13 solved using NMR, we consider the extent to which different parameter sets of the GROMOS protein force field can be distinguished based on comparing a range of structural criteria, including the number of backbone hydrogen bonds, the number of native hydrogen bonds, polar and nonpolar solvent-accessible surface area, radius of gyration, the prevalence of secondary structure elements, J-coupling constants, nuclear Overhauser effect (NOE) intensities, positional root-mean-square deviations (RMSD), and the distribution of backbone ϕ and ψ dihedral angles. It is shown that while statistically significant differences between the average values of individual metrics could be detected, these were in general small. Furthermore, improvements in agreement in one metric were often offset by loss of agreement in another. The work establishes a framework and test set against which protein force fields can be validated. It also highlights the danger of inferring the relative quality of a given force field based on a small range of structural properties or small number of proteins.

Improved random forest classification model combined with C5.0 algorithm for vegetation feature analysis in non-agricultural environments

In response to the challenges posed by the high computational complexity and suboptimal classification performance of traditional random forest algorithms when dealing with high-dimensional and noisy non-agricultural vegetation satellite data, this paper proposes an enhanced random forest algorithm based on the C5.0 algorithm. The paper focuses on the Liaohe Plain, selecting two distinct non-agricultural landscape patterns in Shenbei New District and Changtu County as research objects. High-resolution satellite data from GF-2 serves as the experimental dataset. This paper introduces an ensemble feature method based on the bagging concept to improve the original random forest classification model. This method enhances the likelihood of selecting features beneficial to classifying positive class samples, avoiding excessive removal of useful features from negative samples. This approach ensures feature importance and model diversity. The C5.0 algorithm is then employed for feature selection, and the enhanced vegetation index (EVI) is utilized for vegetation coverage estimation. Results indicate that employing a multi-scale parameter selection tool, combined with limited field-measured data, facilitates the identification and classification of plant species in forest landscapes. The C5.0 algorithm effectively selects classification features, minimizing information redundancy. The established object-oriented random forest classification model achieves an impressive accuracy of 94.02% on the aerial imagery for forest classification dataset, with EVI-based vegetation coverage estimation demonstrating high accuracy. In experiments on the same test set, the proposed algorithm attains an average accuracy of 90.20%, outperforming common model algorithms such as bidirectional encoder representation from transformer, FastText, and convolutional neural network, which achieve average accuracies ranging from 84.41 to 88.33% in identifying non-agricultural artificial habitat vegetation features. The proposed algorithm exhibits a competitive edge compared to other algorithms. These research findings contribute scientific evidence for protecting agricultural ecosystems and restoring agricultural ecosystem biodiversity.

Gradient-Based Saliency Maps Are Not Trustworthy Visual Explanations of Automated AI Musculoskeletal Diagnoses.

Saliency maps are popularly used to "explain" decisions made by modern machine learning models, including deep convolutional neural networks (DCNNs). While the resulting heatmaps purportedly indicate important image features, their "trustworthiness," i.e., utility and robustness, has not been evaluated for musculoskeletal imaging. The purpose of this study was to systematically evaluate the trustworthiness of saliency maps used in disease diagnosis on upper extremity X-ray images. The underlying DCNNs were trained using the Stanford MURA dataset. We studied four trustworthiness criteria-(1) localization accuracy of abnormalities, (2) repeatability, (3) reproducibility, and (4) sensitivity to underlying DCNN weights-across six different gradient-based saliency methods (Grad-CAM (GCAM), gradient explanation (GRAD), integrated gradients (IG), Smoothgrad (SG), smooth IG (SIG), and XRAI). Ground-truth was defined by the consensus of three fellowship-trained musculoskeletal radiologists who each placed bounding boxes around abnormalities on a holdout saliency test set. Compared to radiologists, all saliency methods showed inferior localization (AUPRCs: 0.438 (SG)-0.590 (XRAI); average radiologist AUPRC: 0.816), repeatability (IoUs: 0.427 (SG)-0.551 (IG); average radiologist IOU: 0.613), and reproducibility (IoUs: 0.250 (SG)-0.502 (XRAI); average radiologist IOU: 0.613) on abnormalities such as fractures, orthopedic hardware insertions, and arthritis. Five methods (GCAM, GRAD, IG, SG, XRAI) passed the sensitivity test. Ultimately, no saliency method met all four trustworthiness criteria; therefore, we recommend caution and rigorous evaluation of saliency maps prior to their clinical use.

Interpretable and explainable hybrid model for daily streamflow prediction based on multi-factor drivers.

Streamflow time series data typically exhibit nonlinear and nonstationary characteristics that complicate precise estimation. Recently, multifactorial machine learning (ML) models have been developed to enhance the performance of streamflow predictions. However, the lack of interpretability within these ML models raises concerns about their inner workings and reliability. This paper introduces an innovative hybrid architecture, the TCN-LSTM-Multihead-Attention model, which combines two layers of temporal convolutional networks (TCN) followed by one layer of long short-term memory (LSTM) units, integrated with a Multihead-Attention mechanism for predicting streamflow with streamflow causation-driven prediction samples (RCDP), employing local and global interpretability studies through Shapley values and partial dependency analysis. The find_peaks method was used to identify peak flow events in the test dataset, validating the model's generality and uncovering the physical causative patterns of streamflow. The results show that (1) compared to the LSTM model with the same hyperparameter settings, the proposed TCN-LSTM-Multihead-Attention hybrid model increased the R2 by 52.9%, 2.5%, 43.1%, and 10.7% respectively at four stations in the test set predictions using RCDP samples. Moreover, comparing the prediction results of the hybrid model under different samples in Hengshan station, the R2 for RCDP increased by 5.06% and 1.22% compared to streamflow autoregressive prediction samples (RAP) and meteorological-soil volumetric water content coupled autoregressive prediction samples (MCSAP) respectively. (2) Historical streamflow data from the preceding 3days predominantly influences predictions due to strong autocorrelation, with flow quantity (Q) typically emerging as the most significant feature alongside precipitation (P), surface soil moisture (SSM), and adjacent station flow data. (3) During periods of low and normal flow, historical data remains the most crucial factor; however, during flood periods, the roles of upstream inflow and precipitation become significantly more pronounced. This model facilitates the identification and quantification of various hydrodynamic impacts on flow predictions, including upstream flood propagation, precipitation, and soil moisture conditions. It also elucidates the model's nonlinear relationships and threshold responses, thereby enhancing the interpretability and reliability of streamflow predictions.

Artificial intelligence to automate assessment of ocular and periocular measurements.

To develop and validate a deep learning facial landmark detection network to automate the assessment of periocular anthropometric measurements. Patients presenting to the ophthalmology clinic were prospectively enrolled and had their images taken using a standardised protocol. Facial landmarks were segmented on the images to enable calculation of marginal reflex distance (MRD) 1 and 2, palpebral fissure height (PFH), inner intercanthal distance (IICD), outer intercanthal distance (OICD), interpupillary distance (IPD) and horizontal palpebral aperture (HPA). These manual segmentations were used to train a machine learning algorithm to automatically detect facial landmarks and calculate these measurements. The main outcomes were the mean absolute error and intraclass correlation coefficient. A total of 958 eyes from 479 participants were included. The testing set consisted of 290 eyes from 145 patients. The AI algorithm demonstrated close agreement with human measurements, with mean absolute errors ranging from 0.22 mm for IPD to 0.88 mm for IICD. The intraclass correlation coefficients indicated excellent reliability (ICC > 0.90) for MRD1, MRD2, PFH, OICD, IICD, and IPD, while HPA showed good reliability (ICC 0.84). The landmark detection model was highly accurate and achieved a mean error rate of 0.51% and failure rate at 0.1 of 0%. The automated facial landmark detection network provided accurate and reliable periocular measurements. This may help increase the objectivity of periocular measurements in the clinic and may facilitate remote assessment of patients with tele-health.

A Pilot, Predictive Surveillance Model in Pharmacovigilance Using Machine Learning Approaches.

The identification of a new adverse event (AE) caused by a drug product is one of the key activities in the pharmaceutical industry to ensure the safety profile of a drug product. Machine learning (ML) has the potential to assist with signal detection and supplement traditional pharmacovigilance (PV) surveillance methods. This pilot ML modeling study was designed to detect potential safety signals for two AbbVie products and test the model's capability of detecting safety signals earlier than humans. Drug X, a mature product with post-marketing data, and Drug Y, a recently approved drug in another therapeutic area, were selected. Gradient boosting-based ML approaches (e.g., XGBoost) were applied as the main modeling strategy. For Drug X, eight true signals were present in the test set. Among 12 potential new signals generated, four were true signals with a 50.0% sensitivity rate and a 33.3% positive predictive value (PPV) rate. Among the remaining eight potential new signals, one was confirmed as a signal and detected six months earlier than humans. For Drug Y, nine true signals were present in the test set. Among 13 potential new signals generated, five were true signals with a 55.6% sensitivity rate and a 38.5% PPV rate. Among the remaining eight potential new signals, none were confirmed as true signals upon human review. This model demonstrated acceptable accuracy for safety signal detection and potential for earlier detection when compared to humans. Expert judgment, flexibility, and critical thinking are essential human skills required for the final, accurate assessment of adverse event cases.

Classification of ECG Signals Using Machine Learning Techniques

Cardiovascular diseases are one of the leading causes of mortality in contemporary society. With the growth in the accumulation of medical data, new opportunities have arisen to enhance diagnostic accuracy using machine learning techniques. Heart diseases present symptoms that can be similar to other disorders or be mistaken for signs of aging. Furthermore, diagnosing based on electrocardiogram (ECG) signals can be challenging due to the variability in signal length and characteristics. This article has developed a methodology for classifying ECG signals using the k-Nearest Neighbor (kNN) algorithm and statistical techniques. 9000 ECG signal samples from the PhysioNet database were processed. The signals were normalized to a length of 9000 samples, and relevant features for classification, such as median, standard deviation, skewness, among others, were extracted. Multiple kNN models with different parameters were trained and evaluated on a test set. The models exhibited high performance in classifying normal signals but faced difficulties in correctly classifying signals with arrhythmias. The weighted kNN algorithm demonstrated the best accuracy, although all models showed a tendency to misclassify abnormal signals due to data imbalance. While significant accuracy was achieved in ECG signal classification, there is still room for improvement. Future strategies could involve extracting more relevant features, addressing data imbalance, and fine-tuning model hyperparameters. Integrating domain knowledge from the medical field and advanced signal processing techniques could further enhance classification accuracy.     Received: 3 January 2024 / Accepted: 7 April 2024 / Published: 5 May 2024

205 Detection of anemic sheep using ocular conjunctiva images and deep learning technology

Abstract In sheep, severe anemia often results from gastrointestinal nematodes infections, commonly caused by Haemonchus contortus, a blood-sucking nematode. The objective of the study was to develop a model to predict packed cell volume (PCV) in sheep through ocular conjunctiva images as a real-time anemia diagnosis approach. We collected 3,291 ocular conjunctiva images from 392 sheep on three different farms using an iPhone 8 (12-megapixel). To identify the region of interest in the images (ocular conjunctiva), we annotated 480 images using the Segment Anything Model (SAM). Subsequently, we employed an image segmentation algorithm based on U-net, utilizing the original images and annotations obtained from SAM. We then cropped the segmented images to retain only the region containing the ocular conjunctiva. These cropped and segmented images were used as input data, with PCV as the target variable, in both classification and regression models, while exploring three different deep neural network (DNN) architectures: VGG19, Inception v3, and Xception. We set a threshold of 27% (anemic < 27%, non-anemic ≥ 27%) to convert PCV into a binary variable for classification models. The dataset was split in train, validation and test sets using random by sheep strategy to perform the analysis of the segmentation and prediction models. The segmentation was tested using intersection over union (IoU) as an evaluation metric. To assess and compare predictive quality in the test set, we calculated accuracy, precision, recall, and F1 score for classification models, and R2 for regression models. The U-net segmentation model demonstrated good localization accuracy and reliable segmentation ability, with an average IoU of 0.93, 0.84 and 0.68 in the train, validation and test set, respectively. VGG19 outperformed the other models in classifying individuals as anemic or non-anemic, achieving an F1 score of 0.60, indicating its moderate ability to distinguish between these two classes. For regression, Xception provided the best performance with an R2 of 0.29, suggesting that 29% of the variance in PCV can be explained by the model’s predictions. This innovative approach not only expands the possibilities for integrated high-throughput phenotyping through computer vision but also aids in identifying anemic animals. The results suggest that using ocular conjunctiva images and DNN technology can contribute to support farm-level management decisions, and potentially reduce economic losses due to parasitic infections like H. contortus. Furthermore, this approach reduces the cost of blood tests for anemia identification while allowing for real-time anemia diagnosis.

230 Using phenotypes with uncertain identification leveraging computer vision models

Abstract There has been an increase in high throughput phenotyping using sensors in livestock farming. The major obstacle when analyzing phenotypic data in studies using cameras is identifying the animals using computer vision. To overcome this problem, an analysis method that uses keypoint detection in conjunction with classification algorithms can identify animals in images. However, these algorithms generally output a prediction probability for different classes, reflecting uncertainty. In most practices, the highest probability will be assigned as the putative identification (ID). In some cases, this may introduce incorrect identification and affect downstream decisions. Here, we propose an analytical approach to incorporate all the uncertainty in the putative ID distribution into downstream inferences. We used a publicly available keypoint detection dataset of 17 keypoints detected in 3,226 images from 30 diverse thoroughbred horses. We performed feature engineering with Linear Discriminant Analysis (LDA) using 5-fold cross-validation to build horse classifiers. The best model selected had an accuracy of 88.12% to identify animals in the testing set. Afterward, we performed Monte Carlo simulations to add a phenotype that was assumed independent of the image keypoints. This generated a set of plasmodes to evaluate methods that accounted for ID uncertainty. Each simulation included different means for two groups of 15 horses. In total nine simulation scenarios were considered including varying effect sizes of group mean difference and various repeatability values (horse variance to total variance ratio). We analyzed the simulated data with three different mixed models: 1) Fixed and random effects were based on using ground truth assignment of ID, 2) Fixed and random effects were based on using the putative ID with the highest posterior probability from the best classifier, 3) Finally, prediction probabilities were used to account for putative ID uncertainty. As expected, the model that used ground truth ID had the highest power to detect group differences, and it had the lowest root mean square error (rMSE) to estimate variance components across all scenarios. The linear model that used highest posterior probability putative ID, showed the lowest power and highest rMSE. For instance, as shown in Table 1, the mean rMSE value for group mean differences in the model using ground truth is 0.01 and the model using highest posterior probabilities is 0.13. The model that incorporated ID uncertainty had intermediate properties, showing a rMSE of 0.03 for group differences. Overall, incorporating uncertainty in Animal ID results in improved performance in ultimate test statistics used in animal studies. This is very important in breeding and precision livestock farming contexts where animal identification is challenging. We will further investigate this.

231 Anomaly detection for breed purity analysis

Abstract Recent advancements in genotyping technologies have revolutionized our ability to estimate breed composition in pigs. The classic compositional regression used for this purpose requires a multibreed panel to detect crossbreeding and its application is limited to breed in the panel. In this study, we presented a protocol based on classic anomaly detection algorithms that use single breed panels to detect crossbreeding. By utilizing these methods, we aim to identify and assess breed outliers within large pig genotype panels. The dataset consisted of 44,616 SNP genotypes recorded in purebred Large White, Landrace, Duroc, Pietrain (n = 2,000 from each breed) and 1,010 crossbred pigs (50% Pietrain, 25% Large White, 25% Landrace). Large White pigs (n = 1,500) were randomly selected as reference set. The remaining 500 Large White pigs, other purebred pigs, crossbred pigs, and simulated crossbred animals of known proportion of Large White segments were test sets. We applied two anomaly detection methods: 1) Principal Component Analysis (PCA) and 2) deep learning autoencoders (AE). For PCA the top 600 eigen vectors capturing 85% cumulative proportion of variance in the reference set were selected. Test set genotypes were projected onto the principal components and then they were reconstructed to the full genotype set. For AE, we used a previously published sparse convolutional denoising autoencoder (SCDA) that consisted of two main components: an encoder and a decoder. The encoder compressed the genotypes to 128 components and the decoder reconstructed the set to the original 44,616 SNP. We utilized the model trained in the reference set to reconstruct test set genotypes of the same breed, of alternative breed and of crossbred. Accuracy of reconstruction of genotypes was computed using correlation and mean square error. The proportion of rejected genotypes was used to assess the ability of anomaly detection algorithms to detect crossbreeding. For each criterion, the threshold to reject genotypes was selected as the value at which 5% of Large White animals were rejected (specificity = 0.95). Results are summarized in (Table 1). PCA provided better properties to detect genomic outliers compared with AE. PCA anomaly detection was able to detect purebred individuals with probability 1.0. It also detected over 90% of crossbred individuals that were 75% Large Whites. Crossbred with a lower proportion of Large White genomic segments were detected with P < 0.5. Anomaly detection algorithms can be used to detect genomic outliers but with relatively low sensitivity. More work is needed to optimize these algorithms for breeding applications.

Interpretable machine learning based on CT-derived extracellular volume fraction to predict pathological grading of hepatocellular carcinoma.

To develop a non-invasive auxiliary assessment method based on CT-derived extracellular volume (ECV) to predict the pathological grading (PG) of hepatocellular carcinoma (HCC). The study retrospectively analyzed 238 patients who underwent HCC resection surgery between January 2013 and April 2023. Six machine learning algorithms were employed to construct predictive models for HCC PG: logistic regression, extreme gradient boosting, Light Gradient Boosting Machine (LightGBM), random forest, adaptive boosting, and Gaussian naive Bayes. Model performance was evaluated using receiver operating characteristic curve analysis, including area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and F1 score. Calibration plots were used for visual evaluation of model calibration. Clinical decision curve analysis was performed to assess potential clinical utility by calculating net benefit. 166 patients from Hospital A were allocated to the training set, while 72 patients from Hospital B (constituting 30.25% of the total sample) were assigned to the test set. The model achieved an AUC of 1.000 (95%CI: 1.000-1.000) in the training set and 0.927 (95%CI: 0.837-0.999) in the validation set, respectively. Ultimately, the model achieved an AUC of 0.909 (95%CI: 0.837-0.980) in the test set, with an accuracy of 0.778, sensitivity of 0.906, specificity of 0.789, negative predictive value of 0.556, and F1 score of 0.908. This study successfully developed and validated a non-invasive auxiliary assessment method based on CT-derived ECV to predict the HCC PG, providing important supplementary information for clinical decision-making.

Composition and state prediction of lithium-ion cathode via convolutional neural network trained on scanning electron microscopy images

High-throughput materials research is strongly required to accelerate the development of safe and high energy-density lithium-ion battery (LIB) applicable to electric vehicle and energy storage system. The artificial intelligence, including machine learning with neural networks such as Boltzmann neural networks and convolutional neural networks (CNN), is a powerful tool to explore next-generation electrode materials and functional additives. In this paper, we develop a prediction model that classifies the major composition (e.g., 333, 523, 622, and 811) and different states (e.g., pristine, pre-cycled, and 100 times cycled) of various Li(Ni, Co, Mn)O2 (NCM) cathodes via CNN trained on scanning electron microscopy (SEM) images. Based on those results, our trained CNN model shows a high accuracy of 99.6% where the number of test set is 3840. In addition, the model can be applied to the case of untrained SEM data of NCM cathodes with functional electrolyte additives.

Data leakage in deep learning studies of translational EEG

A growing number of studies apply deep neural networks (DNNs) to recordings of human electroencephalography (EEG) to identify a range of disorders. In many studies, EEG recordings are split into segments, and each segment is randomly assigned to the training or test set. As a consequence, data from individual subjects appears in both the training and the test set. Could high test-set accuracy reflect data leakage from subject-specific patterns in the data, rather than patterns that identify a disease? We address this question by testing the performance of DNN classifiers using segment-based holdout (in which segments from one subject can appear in both the training and test set), and comparing this to their performance using subject-based holdout (where all segments from one subject appear exclusively in either the training set or the test set). In two datasets (one classifying Alzheimer's disease, and the other classifying epileptic seizures), we find that performance on previously-unseen subjects is strongly overestimated when models are trained using segment-based holdout. Finally, we survey the literature and find that the majority of translational DNN-EEG studies use segment-based holdout. Most published DNN-EEG studies may dramatically overestimate their classification performance on new subjects.

Assessing Endoscopic Response in Locally Advanced Rectal Cancer Treated with Total Neoadjuvant Therapy: Development and Validation of a Highly Accurate Convolutional Neural Network.

Rectal tumors display varying degrees of response to total neoadjuvant therapy (TNT). We evaluated the performance of a convolutional neural network (CNN) in interpreting endoscopic images of either a non-complete response to TNT or local regrowth during watch-and-wait surveillance. Endoscopic images from stage II/III rectal cancers treated with TNT from 2012 to 2020 at a single institution were retrospectively reviewed. Images were labelled as Tumor or No Tumor based on endoscopy timing (before, during, or after treatment) and the tumor's endoluminal response. A CNN was trained using ResNet-50 architecture. The area under the curve (AUC) was analyzed during training and for two test sets. The main test set included images of tumors treated with TNT. The other contained images of local regrowth. The model's performance was compared to sixteen surgeons and surgical trainees who evaluated 119 images for evidence of tumor. Fleiss' kappa was calculated by respondent experience level. A total of 2717 images from 288 patients were included; 1407 (51.8%) contained tumor. The AUC was 0.99, 0.98, and 0.92 for training, main test, and local regrowth test sets. The model performed on par with surgeons of all experience levels for the main test set. Interobserver agreement was good ( = 0.71-0.81). All groups outperformed the model in identifying tumor from images of local regrowth. Interobserver agreement was fair to moderate ( = 0.24-0.52). A highly accurate CNN matched the performance of colorectal surgeons in identifying a noncomplete response to TNT. However, the model demonstrated suboptimal accuracy when analyzing images of local regrowth.

Malware Detection in Files and URL’s Using Machine Learning

The rampant extrusion of personal or sensitive data by malicious software has inflicted significant damage worldwide, posing a critical threat to individuals and various sectors of society. Cyber attackers engage in relentless warfare against computer systems, deploying malware to breach crucial data. Consequently, safeguarding this data has become a paramount concern for researchers. This paper aims to address this challenge by proposing the development of an application leveraging machine learning algorithms to discern between malicious and legitimate files effectively. In the contemporary era, marked by pervasive technological advancements, the widespread adoption of the internet has facilitated unprecedented connectivity but also heightened the risk of cyber threats perpetrated by malicious actors. These threats, often executed through the deployment of malware, have inflicted substantial financial losses, amounting to billions of dollars globally. Consequently, safeguarding against malicious attacks has become a paramount endeavor in the ongoing battle against cybercrime. To address this imperative, this paper endeavors to leverage machine learning algorithms to prognosticate the likelihood of malware infections in computer systems, utilizing a supervised learning approach alongside gradient boosting algorithms. Drawing upon a publicly available dataset, meticulously partitioned into training and testing sets, the study embarks on four distinct experiments employing the aforementioned algorithms. Notably, the findings underscore Light GBM as the most efficacious model, boasting an impressive AUC Score of 0.73926, thus signifying a pivotal advancement in fortifying cyber defenses through predictive analytics. Malware poses a significant threat in today's digital landscape, with various forms proliferating over the last decade, leading to substantial financial losses for organizations. Defined as malicious software, malware wreaks havoc on users' computers through various means. To address this challenge, a solution leveraging machine learning techniques has been proposed to detect malware in downloaded files. By analyzing features such as MD5 hash, Optional Header size, and Load Configuration Size, different machine learning algorithms are trained to distinguish between malicious and benign files effectively. Through rigorous training and comparison using Validation and Test datasets, the SVM Classifier emerged as the most accurate, achieving up to 99.99% accuracy in detecting malware types like Adware, Trojan, Backdoors, Unknown, Multidrop, Rbot, Spam, and Ransomware. This approach not only identifies malware but also mitigates potential risks to users' systems, safeguarding against detrimental impacts from infections.

Establishment of a mortality risk nomogram for predicting in-hospital mortality of sepsis: cohort study from a Chinese single center

ObjectiveTo establish a mortality risk nomogram for predicting in-hospital mortality of sepsis patients in the Chinese population.MethodsData were obtained from the medical records of sepsis patients enrolled at the Affiliated Huadu Hospital, Southern Medical University, between 2019 and 2021. A total of 696 sepsis patients were initially included in our research, and 582 cases were finally enrolled after screening and divided into the survival group (n = 400) and the non-survival group (n = 182) according to the incidence of mortality during hospitalization. Twenty-eight potential sepsis-related risk factors for mortality were identified. Least absolute shrinkage and selection operator (LASSO) regression was used to optimize variable selection by running cyclic coordinate descent with k-fold (tenfold in this case) cross-validation. We used binary logistic regression to build a model for predicting mortality from the variables based on LASSO regression selection. Binary logistic regression was used to establish a nomogram based on independent mortality risk factors. To validate the prediction accuracy of the nomogram, receiver operating characteristic curve (ROC) analysis, decision curve analysis (DCA) and restricted cubic spline (RCS) analysis were employed. Eventually, the Hosmer-Lemeshow test and calibration curve were used for nomogram calibration.ResultsLASSO regression identified a total of ten factors, namely, chronic heart disease (CHD), lymphocyte count (LYMP), neutrophil-lymphocyte ratio (NLR), red blood cell distribution width (RDW), C reactive protein (CRP), Procalcitonin (PCT), lactic acid, prothrombin time (PT), alanine aminotransferase (ALT), total bilirubin (Tbil), interleukin-6 (IL6), that were incorporated into the multivariable analysis. Finally, a nomogram including CHD, LYMP, NLR, RDW, lactic acid, PT, CRP, PCT, Tbil, ALT, and IL6 was established by multivariable logistic regression. The ROC curves of the nomogram in the training and validation sets were 0.9836 and 0.9502, respectively. DCA showed that the nomogram could be applied clinically if the risk threshold was between 29.52 and 99.61% in the training set and between 31.32 and 98.49% in the testing set. RCS showed that when the value of independent risk factors from the predicted model exceeded the median, the mortality hazard ratio increased sharply. The results of the Hosmer–Lemeshow test (χ2 = 0.1901, df = 2, p = 0.9091) and the calibration curves of the training and validation sets showed good agreement with the actual results, which indicated good stability of the model.ConclusionOur nomogram, including CHD, LYMP, NLR, RDW, lactic acid, PT, CRP, PCT, Tbil, ALT, and IL6, exhibits good performance for predicting mortality risk in adult sepsis patients.

Automated detection of anterior crossbite on intraoral images and videos utilizing deep learning.

Malocclusion has emerged as a burgeoning global public health concern. Individuals with an anterior crossbite face an elevated risk of exhibiting characteristics such as a concave facial profile, negative overjet, and poor masticatory efficiency. In response to this issue, we proposed a convolutional neural network (CNN)-based model designed for the automated detection and classification of intraoral images and videos. A total of 1865 intraoral images were included in this study, 1493 (80%) of which were allocated for training and 372 (20%) for testing the CNN. Additionally, we tested the models on 10 videos, spanning a cumulative duration of 124 seconds. To assess the performance of our predictions, metrics including accuracy, sensitivity, specificity, precision, F1-score, area under the precision-recall (AUPR) curve, and area under the receiver operating characteristic (ROC) curve (AUC) were employed. The trained model exhibited commendable classification performance, achieving an accuracy of 0.965 and an AUC of 0.986. Moreover, it demonstrated superior specificity (0.992 vs. 0.978 and 0.956, P < 0.05) in comparison to assessments by two orthodontists. Conversely, the CNN model displayed diminished sensitivity (0.89 vs. 0.96 and 0.92, P < 0.05) relative to the orthodontists. Notably, the CNN model accomplished a perfect classification rate, successfully identifying 100% of the videos in the test set. The deep learning (DL) model exhibited remarkable classification accuracy in identifying anterior crossbite through both intraoral images and videos. This proficiency holds the potential to expedite the detection of severe malocclusions, facilitating timely classification for appropriate treatment and, consequently, mitigating the risk of complications.

A Comparative Dataset of Annotated Broccoli Heads Recorded with Depth Cameras from a Moving Vehicle

An extensive, publicly available dataset is presented—the LAR Broccoli dataset—which contains 20,000 manually annotated images of broccoli heads captured from a moving tractor at an organic farm in the UK. The dataset contains images of the same row of broccoli heads recorded at 30 frames per second (fps) with three different cameras. Two off-the-shelf, relatively low-cost depth-sensing cameras were used, with the tractor moving at a speed of around 1 km/h, in addition to a webcam, with the tractor moving twice as fast. The utility of the dataset is demonstrated in four ways. First, three different state-of-the-art detector models were trained on the dataset, achieving an overall mean Average Precision (mAP) score of over 95% for the best-performing detector. The results validate the utility of the dataset for the standard task of in-field broccoli head recognition. Second, experiments with transfer learning were conducted, initialised with a smaller pre-trained broccoli detection model, and refined with the LAR Broccoli dataset. Third, we assessed the advantages of transfer learning not only using mAP but also according to time and space requirements for training models, which provides a proxy metric for energy efficiency, a practical consideration for real-world model training. Fourth, the cross-camera generalisation among the three camera systems was compared. The results highlight that testing and training detector models using different camera systems can lead to reduced performance, unless the training set also includes some images captured in the same manner as those in the test set.