Automatic ROI Selection with a Reliability Evaluation Method for Cirrhosis Detection Using Ultrasound Images

Study question Can an AI image-based model be trained to predict whether an endometrium, prior to progesterone, is receptive for successful embryo implantation from ultrasound images? Summary answer An endometrial receptivity AI model, utilizing ultrasound images and clinical features, is predictive of successful implantation and outperforms endometrial thickness as a predictor of implantation. What is known already Despite its importance in successful embryo implantation and subsequent live birth, endometrial receptivity is difficult to measure. The standard of care at some clinics is to cancel an embryo transfer if EMT is measuring less than 7 mm on ultrasound assessment – however, achieving an EMT threshold does not guarantee success, even with euploid blastocysts. Currently available receptivity tests (e.g. endometrial receptivity assays) are unreliable and have not shown significant improvements in reproductive outcomes, necessitating the development of new evaluation methods for endometrial receptivity. AI can potentially address this gap in clinical care and understanding, aiming to increase patient success. Study design, size, duration 79,602 ultrasound images (40,910 patients) of the endometrium, on the day of progesterone start according to patient’s frozen embryo transfer cycle, were retrospectively collected from 4 clinic networks including 70 clinic locations. PGT-A outcomes and blastocyst quality were available for 15,466 and 36,070 blastocysts respectively, with 14,538 having labels for both. Different image samples (based on embryo quality) were selected to train and test several AI models, with implantation (positive beta-hCG) as a binary outcome. Participants/materials, setting, methods Three criteria for negative (implantation) class sample selection were tested for receptivity model development: 1) high- or medium-quality and/or euploid blastocysts; 2) high-quality blastocysts; 3) all blastocysts. Models were ensembles consisting of an image-based deep learning (DL)-model and a feature-based machine learning (ML)-model. Model performance was measured by Area-under-the-curve (AUC), sensitivity, and specificity. Feature importance from the ML model was assessed, while receptivity model performance was compared to the power of EMT to predict implantation. Main results and the role of chance A receptivity model trained on scenario 1) (n = 27,424) achieved AUC 0.613, sensitivity 0.716, and specificity 0.439 on a test set of 9197 samples, in predicting implantation. Another receptivity model trained on scenario 2) (n = 25,625) achieved AUC 0.627, sensitivity 0.637, and specificity 0.543 on a test set of 8642. Whereas a third receptivity model trained on scenario 3) (n = 31,238) was selected for further analysis as it attained the best performance with AUC 0.631, sensitivity 0.628, and specificity 0.556 on a test set of 10,422. The ensemble model also utilizes relevant clinical features, which were ranked by importance to model predictions: EMT; progesterone blood test value; age at transfer; previous total embryo transfers; days between ultrasound date and transfer date; age at retrieval; and oocyte origin. EMT was assessed for its ability to predict implantation at thresholds between 5–20 mm. The threshold that best separates positive and negative implantation was determined by evaluating AUC on a validation set of 5682. A threshold of 8.8 mm achieved an AUC 0.576, sensitivity 0.694, and specificity 0.455 in predicting implantation on a test set of 5686. However, the receptivity model AUC (0.631) was significantly higher (p < 0.001; DeLong test for differences in AUC), outperforming EMT predictability. Limitations, reasons for caution The endometrial receptivity model was built and tested on retrospective frozen embryo transfer data. Results should be corroborated in prospective and non-selection studies, as well as in multiple geographical locations (all clinics included reside in the United States). The dataset requires greater diversification, with a greater representation of EMT (<7mm). Wider implications of the findings This study indicates that an AI model can be built to predict implantation from ultrasound images and clinical features, surpassing current standards - EMT alone. Further experimentation, with additional clinical features, may improve performance to facilitate development of a model that can accurately predict the gold-standard of live birth. Trial registration number Not applicable

BackgroundTo investigate the optimal periendometrial zone (PEZ) in ultrasound (US) images and assess the performance of ultrasound radiomics in predicting the outcome of frozen embryo transfer (FET).MethodsThis prospective study had 422 female participants (training set: n = 358, external validation set: n = 64). We delineated the region of interest (ROI) of the endometrium (EN) from ultrasound images of the median sagittal surface of the uteri of patients. We determined the ROIs of PEZ on US images by automatically expanding 2.0, 4.0, 6.0, and 8.0 mm radii surrounding the EN. We determined the radiomics characteristics based on the ROIs of the endometrium and PEZ, then compared the different sizes of PEZ to determine the optimal PEZ. We constructed models of the EN and optimal PEZ using six machine learning algorithms. We developed a combined model using the radiomics characteristics of EN and the optimal PEZ. We evaluated the performance of the three models using the area under the curve (AUC).ResultsThe optimal PEZ was 4.0 mm with a maximum AUC of 0.715 (95% confidence interval (CI): 0.581 – 0.833) in the external validation set. The combined radiomics model (endometrium and PEZ4.0 mm) yielded the best predictive performance with AUC = 0.853 (95% CI: 0.811 – 0.890) for the training set and AUC = 0.809 (95% CI: 0.696 – 0.909) for the external validation set.ConclusionsPEZ4.0 mm could be the optimal area for predicting clinical pregnancy after FET. An US-based radiomics model that combines EN and PEZ4.0 mm demonstrated strong potential in helping clinicians predict FET outcomes more accurately, thereby supporting informed decision-making before treatment.

Prediction of Renal Function 1 Year After Transplantation Using Machine Learning Methods Based on Ultrasound Radiomics Combined With Clinical and Imaging Features.

Background:Systemic lupus erythematosus (SLE) is characterised by the production of a multitude of autoantibodies (Abs), yet the antigen specificities remain elusive for most of these Abs. The diagnosis of SLE...

Early and reliable prediction of shunt-dependent hydrocephalus (SDHC) after aneurysmal subarachnoid hemorrhage (aSAH) may decrease the duration of in-hospital stay and reduce the risk of catheter-associated meningitis. Machine learning (ML) may improve predictions of SDHC in comparison to traditional non-ML methods. ML models were trained for CHESS and SDASH and two combined individual feature sets with clinical, radiographic, and laboratory variables. Seven different algorithms were used including three types of generalized linear models (GLM) as well as a tree boosting (CatBoost) algorithm, a Naive Bayes (NB) classifier, and a multilayer perceptron (MLP) artificial neural net. The discrimination of the area under the curve (AUC) was classified (0.7 ≤ AUC < 0.8, acceptable; 0.8 ≤ AUC < 0.9, excellent; AUC ≥ 0.9, outstanding). Of the 292 patients included with aSAH, 28.8% (n = 84) developed SDHC. Non-ML-based prediction of SDHC produced an acceptable performance with AUC values of 0.77 (CHESS) and 0.78 (SDASH). Using combined feature sets with more complex variables included than those incorporated in the scores, the ML models NB and MLP reached excellent performances, with an AUC of 0.80, respectively. After adding the amount of CSF drained within the first 14 days as a late feature to ML-based prediction, excellent performances were reached in the MLP (AUC 0.81), NB (AUC 0.80), and tree boosting model (AUC 0.81). ML models may enable clinicians to reliably predict the risk of SDHC after aSAH based exclusively on admission data. Future ML models may help optimize the management of SDHC in aSAH by avoiding delays in clinical decision-making.

Noninvasive Prediction of Lymph Node Status for Patients with Early-Stage Cervical Cancer Based on Radiomics Features from Ultrasound Images

Background and AimTreatment response to ursodeoxycholic acid may predict the prognosis of patients with primary biliary cholangitis (PBC). Recent studies have suggested the benefits of using machine learning (ML) to forecast complex medical predictions. We aimed to predict treatment response in patients with PBC using ML and pretreatment data.MethodsWe conducted a single‐center retrospective study and collected data from 194 patients with PBC who were followed up for at least 12 months after treatment initiation. Patient data were analyzed with five ML models, namely random forest, extreme gradient boosting (XGB), decision tree, naïve Bayes, or logistic regression, to predict treatment response using the Paris II criteria. The established models were assessed using an out‐of‐sample validation. The area under the curve (AUC) was used to evaluate the efficacy of each algorithm. Overall survival and liver‐related deaths were analyzed using Kaplan–Meier analysis.ResultsCompared to logistic regression (AUC = 0.595, P = 0.0219, 0.031 models), ML analyses showed significantly high AUC in the random forest (AUC = 0.84) and XGB (AUC = 0.83) models; however, the AUC was not significantly high for decision tree (AUC = 0.633) or naïve Bayes (AUC = 0.584) models. Kaplan–Meier analysis showed significantly improved prognoses in patients predicted to achieve the Paris II criteria by XGB (log‐rank = 0.005 and 0.007).ConclusionML algorithms could improve treatment response prediction using pretreatment data, which could lead to better prognoses. In addition, the ML model using XGB could predict the prognosis of patients before treatment initiation.

Racial and Ethnic Disparities in Predictive Accuracy of Machine Learning Algorithms Developed Using a National Database for 30-Day Complications Following Total Joint Arthroplasty

Postherpetic neuralgia (PHN), a complication of herpes zoster, significantly impacts the quality of life of affected patients. Research indicates that early intervention for pain can reduce the occurrence or severity of PHN. This study aims to develop a predictive model and scoring table to identify patients at risk of developing PHN following acute herpetic neuralgia, facilitating informed clinical decision-making. We conducted a retrospective review of 524 hospitalized patients with herpes zoster at The First Affiliated Hospital of Zhejiang Chinese Medical University from December 2020 to December 2023 and classified them according to whether they had PHN, collecting a comprehensive set of 30 patient characteristics and disease-related indicators, 5 comorbidity indicators, 2 disease score values, and 10 serological indicators. Relevant features associated with PHN were identified using the least absolute shrinkage and selection operator (LASSO). Then, the patients were divided into a training set and a test set in a 4:1 ratio, with comparability tested using univariate analysis. Six models were established in the training set using machine learning methods: support vector machines, logistic regression, random forest, k-nearest neighbor, gradient boosting, and neural network. The performance of these models was evaluated in the test set, and a nomogram based on logistic regression was used to create a PHN prediction score table. Eight non-zero characteristic variables selected from the LASSO regression results were included in the model, including age [area under the curve (AUC)=0.812, p<0.001], Numerical Rating Scale (NRS) (AUC=0.792, p<0.001), receiving treatment time (AUC=0.612, p<0.001), rash recovery time (AUC=0.680, p<0.001), history of malignant tumor (AUC=0.539, p<0.001), history of diabetes (AUC=0.638, p<0.001), varicella-zoster virus immunoglobulin M (AUC=0.620, p<0.001), and serum nerve-specific enolase (AUC=0.659, p<0,001). The gradient boosting model outperformed other classifier models on the test set with an AUC of 0.931, 95% confidence interval (CI) (0.882-0.980), accuracy of 0.886 (95% CI 0.809-0.940). In the test set, our predictive scoring table achieved an AUC of 0.820 (95% CI 0.869-0.970) with accuracy of 0.790 (95% CI 0.700-0.864). This study presents a methodology for predicting the development of postherpetic neuralgia in shingles patients by analyzing historical case data, employing various machine learning techniques, and selecting the optimal model through comparative analysis. In addition, a logistic regression model has been used to create a scoring table for predicting the postherpetic neuralgia.

Background:Gastric cancer, characterized by insidious onset and multiple metastasis, is almost incurable and has poor prognosis, and also one of the leading causes of treatment failure and death in patients with gastric cancer (GC). However, the prognosis of collagen type V alpha2 chain (COL5A2) in GC and renal metastasis is unknown.Methods:Recruited 148 patients who underwent GC. The diagnosis of GC was confirmed by ultrasound imaging and pathological examination. Immunohistochemistry and RT-qPCR were performed to exam the expression level of COL5A2. The statistical methods included Pearson chi-square test, Spearman-rho correlation test, univariate and multivariate cox regression analysis. Finally, this research constructed receiver operating characteristic (ROC) curves and applied the area under the curve (AUC).Results:Based on Pearson's chi-square test, Spearman-rho test, and univariate/multivariate cox regression, pathologic grade (P < .001), renal metastasis (P < .001) and staging (P < .001) were significantly related to COL5A2. And COL5A2 expression (hazard ratio [HR]: 18.834, P < .001) is an independent risk factor of GC. The AUC was used as the degree of confidence in judging each factor: COL5A2 (AUC = 0.878, P < .001), COL1A1 (AUC = 0.636, P = .006), COL1A2 (AUC = 0.545, P = .368), and COL3A1 (AUC = 0.617, P = .019). Through the ROC result, COL5A2 had more advantage as a biomarker for GC than other collagens.Conclusions:COL5A2 gene expression level might be a risk factor for GC. COL5A2 has a strong correlation with the prognosis of the disease.

Background: Insulin resistance is a common etiology of metabolic syndrome, but receiver operating characteristic (ROC) curve analysis shows a weak association in Koreans. Using a machine learning (ML) approach, we aimed to generate the best model for predicting insulin resistance in Korean adults aged > 40 of the Ansan/Ansung cohort using a machine learning (ML) approach. Methods: The demographic, anthropometric, biochemical, genetic, nutrient, and lifestyle variables of 8842 participants were included. The polygenetic risk scores (PRS) generated by a genome-wide association study were added to represent the genetic impact of insulin resistance. They were divided randomly into the training (n = 7037) and test (n = 1769) sets. Potentially important features were selected in the highest area under the curve (AUC) of the ROC curve from 99 features using seven different ML algorithms. The AUC target was ≥0.85 for the best prediction of insulin resistance with the lowest number of features. Results: The cutoff of insulin resistance defined with HOMA-IR was 2.31 using logistic regression before conducting ML. XGBoost and logistic regression algorithms generated the highest AUC (0.86) of the prediction models using 99 features, while the random forest algorithm generated a model with 0.82 AUC. These models showed high accuracy and k-fold values (>0.85). The prediction model containing 15 features had the highest AUC of the ROC curve in XGBoost and random forest algorithms. PRS was one of 15 features. The final prediction models for insulin resistance were generated with the same nine features in the XGBoost (AUC = 0.86), random forest (AUC = 0.84), and artificial neural network (AUC = 0.86) algorithms. The model included the fasting serum glucose, ALT, total bilirubin, HDL concentrations, waist circumference, body fat, pulse, season to enroll in the study, and gender. Conclusion: The liver function, regular pulse checking, and seasonal variation in addition to metabolic syndrome components should be considered to predict insulin resistance in Koreans aged over 40 years.

AIM: This study aimed to construct a risk stratification model for prostate cancer (PCa) ultrasound imaging data and machine learning algorithms, with the goal of providing an effective tool for early diagnosis, personalized treatment, and clinical decision-making. METHODS: A total of 211 histopathologically confirmed PCa patients were retrospectively enrolled and categorized into low-risk (n = 65), intermediate-risk (n = 55), and high-risk (n = 91) groups based on prostate-specific antigen levels, Gleason scores, and clinical T stage. From ultrasound images, 135 quantitative radiomic features—including morphological, texture, and edge descriptors—were extracted using the PyRadiomics toolkit. Feature dimensionality was reduced using the Pearson correlation coefficient (PCC), followed by recursive feature elimination (RFE) with 10-fold nested cross-validation to select the most informative features. Three machine learning algorithms—support vector machine (SVM), random forest (RF), and logistic regression (LR)—were trained and evaluated. Model performance was assessed using accuracy, sensitivity, specificity, and area under the curve (AUC). RESULTS: The RF model achieved the best performance in both training and test cohorts, with AUCs of 0.87 and 0.86, and accuracies of 90% and 88%, respectively. DeLong's test confirmed that RF significantly outperformed SVM (p = 0.016) and LR (p = 0.004) in AUC comparison. The RF model also demonstrated robust predictive ability across risk subgroups: in the high-risk group, it achieved an AUC of 0.89, accuracy of 89%, sensitivity of 88%, and specificity of 90%; in the intermediate- and low-risk groups, AUCs were 0.86 and 0.81, respectively. Feature importance analysis revealed that wavelet-transformed Gray Level Dependence Matrix (GLDM) texture features, particularly DependenceEntropy and DependenceVariance, were the most predictive, highlighting the value of intratumoral textural heterogeneity in risk classification. CONCLUSIONS: The RF-based ultrasound radiomics model enables accurate stratification of PCa risk, with remarkable performance in identifying high-risk patients.

The aim of this study was to test the hypothesis that the Computer Aided UltraSound (CAUS) method developed by the authors [1-4] for the estimation of UltraSound Tissue Characteristics (UTC) parameters on transcutaneous (Transc) ultrasound (US) images can predict the liver fat content with similar accuracy and precision as with intraoperative (Intraop) US. A large animal study in post partum dairy cows (N=151) was performed to test these hypotheses. Five Transc B-Mode US liver image were acquired before surgery. During abomasal displacement surgery five Intraop US B-Mode liver images and a liver biopsy was taken. In liver tissue samples, triacylglycerol (TAG) content was measured by biochemical analysis. Firstly the equipment preset, which was kept fixed during whole study time, was carefully calibrated[5]. For the echo level calibration a TMP was used, and all UTC parameters were expressed relatively to those of the phantom. Prior to UTC parameters estimation several pre-processing steps were performed: Back-Scan Conversion (BSC); Look Up Table (LUT) correction; superficial tissue layers (Fat layer) attenuation correction and Automatic Gain Correction (AGC) were performed. Also several postprocessing steps were incorporated like: Automatic segmentation and residual attenuation correction were performed. Stepwise multiple linear regression analysis on a training set (N=76) was performed. In all cases the Residual Attenuation coefficient (ResAtt, R=0.81) was the only selected parameter. The results were tested on the residual cows (test set N=75) to predict the TAG content in the liver. Receiver Operating Characteristics (ROC) analysis then was applied to estimate the Area Under the Curve (AUC) and the sensitivity and specificity of the CAUS method. Equivalent high predictive values for AUC (95%), sensitivity(87%) and specificity (83%) for Intraop and Transc applications were found. Consequently, it can be concluded, applied Fat layer attenuation correction to Transc US images was performed adequately.

Integrating machine learning (ML) models into healthcare systems is a rapidly evolving field with the potential to revolutionize care delivery. This study aimed to classify fertility rates and identify significant predictors using ML models among reproductive women in Ethiopia. This study utilized eight ML models in 5864 reproductive-age women using Ethiopian Demographic Health Survey (EDHS), 2019 data. Phyton programming language was used to develop these models. Predictors of fertility rate were determined using the feature important techniques. The performance of models was evaluated using accuracy, area under the curve (AUC), precision, recall, F1-score, specificity, and sensitivity. The mean age of participants was 32.7 (± 5.6) years. The random forest classifier (accuracy = 0.901 and AUC = 0.961) followed by a one-dimensional convolutional neural network (accuracy = 0.899 and AUC = 0.958), logistic regression (accuracy = 0.874 and AUC = 0.937), and gradient boost classifier (accuracy = 0.851 and AUC 0.927) were the top performing ML models. Family size, age, occupation, and education with an average importance score of 0.198, 0.151, 0.118, and 0.081, respectively were the top significant predictors of the fertility rate. The best ML models to classify and predict fertility rates were random forest, one-dimensional convolutional neural network, logistic regression, and gradient boost classifier. The findings on important factors of fertility rate can inform targeted public health, programs that address disparities related to family size, occupation, education, and other socioeconomic factors.

BackgroundThe study aims to compare the prognostic performance of conventional scoring systems to a machine learning (ML) model on coronary computed tomography angiography (CCTA) to discriminate between the patients with and without major adverse cardiovascular events (MACEs) and to find the most important contributing factor of MACE.Materials and methodsFrom November to December 2019, 500 of 1586 CCTA scans were included and analyzed, then six conventional scores were calculated for each participant, and seven ML models were designed. Our study endpoints were all-cause mortality, non-fatal myocardial infarction, late coronary revascularization, and hospitalization for unstable angina or heart failure. Score performance was assessed by area under the curve (AUC) analysis.ResultsOf 500 patients (mean age: 60 ± 10; 53.8% male subjects) referred for CCTA, 416 patients have met inclusion criteria, 46 patients with early (<90 days) cardiac evaluation (due to the inability to clarify the reason for the assessment, deterioration of the symptoms vs. the CCTA result), and 38 patients because of missed follow-up were not enrolled in the final analysis. Forty-six patients (11.0%) developed MACE within 20.5 ± 7.9 months of follow-up. Compared to conventional scores, ML models showed better performance, except only one model which is eXtreme Gradient Boosting had lower performance than conventional scoring systems (AUC:0.824, 95% confidence interval (CI): 0.701–0.947). Between ML models, random forest, ensemble with generalized linear, and ensemble with naive Bayes were shown to have higher prognostic performance (AUC: 0.92, 95% CI: 0.85–0.99, AUC: 0.90, 95% CI: 0.81–0.98, and AUC: 0.89, 95% CI: 0.82–0.97), respectively. Coronary artery calcium score (CACS) had the highest correlation with MACE.ConclusionCompared to the conventional scoring system, ML models using CCTA scans show improved prognostic prediction for MACE. Anatomical features were more important than clinical characteristics.