P–673 A visualized clinical model predicting cumulative pregnancy rate after IVF-ET: a real-world study

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
M Zhang
Keyword(s):  
External Validation ◽  
Training Dataset ◽  
Validation Dataset ◽  
Single Center ◽  
Threshold Probability ◽  
Internal Validation ◽  
Female Age ◽  
Clinical Model ◽  
Temporal Validation ◽  
Ivf Et

Abstract Study question How is the cumulative pregnancy probability of individual patients after IVF-ET,could we develop a visualized clinical model to predict it based on patient’s characteristics? Summary answer The visualized clinical mode incorporates five items of female age, number of oocytes, antral follicle count, endometrium thickness and basal FSH level. What is known already Many factors can result in infertility, prognosis prediction is clinically relevant for making the right therapeutic strategy while avoiding overtreatment. It is also helpful in counselling, making the patient aware of possible treatment duration and estimated expense and managing patient’s expectation. Visualized clinical mode and accurate prediction would also be helpful in designing clinical trials to evaluate new treatments. Study design, size, duration We conducted a retrospective analysis of a single-center database using prospectively collected data from women who underwent IVF/ICSI treatment from January 2013 to December 2015, All the participants were followed up for at least 2 years, 3538 IVF-ET cycles were included in the study.A total of 3538 IVF/ICSI cycles were included in the study. Participants/materials, setting, methods Data from a total of 2312 IVF/ICSI cycles from January 2013 to December 2014 were randomly split into training dataset (1550, 67%) and internal validation dataset (762, 33%). A total of 1226 IVF/ICSI cycles in 2015 was applied to external validation dataset (temporal validation) Main results and the role of chance Multivariable logistic regression model combined with restricted cubic splines function was used to test independent prognostic factors and estimate their effects on treatment outcome for patients treated with IVF/ICSI. Female age, number of oocytes retrieved, AFC, endometrium thickness and basal FSH were included the final model. The above model was used to calculate prediction scores for all women in the training and validation datasets. The C-index was 0.693 (95% CI: 0.692∼0.695) in training sets, 0.689 in internal validation sets and 0.710 in external validation sets, which denotes a good performance. Calibration curves suggest excellent model calibration, with an ideal agreement between the prediction and actual observation . The DCA showed that if the threshold probability is between 0 and 0.7, using the nomogram derived in the present study to predict cumulative pregnancy provided a greater benefit than either thetreat-all or the treat-none strategy. Limitations, reasons for caution it was a retrospective, single-center study.In the future, prospective, randomized controlled, multicenter clinical studies will be designed. Wider implications of the findings: The visualized nomogram model provides great predictive value for infertility patients in their first IVF/ICSI cycle, and predicts the pregnancy probability of individuals ,and could help clinicians improving clinical counselling. Trial registration number Not applicable

scholarly journals An online diabetic retinopathy screening tool for patients with type 2 diabetes

2021 ◽  
Vol 14 (11) ◽  
pp. 1748-1755
Author(s):  
Wan-Yue Li ◽  
◽  
Ya-Nan Song ◽  
Ling Luo ◽  
Chuang Nie ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Diabetic Retinopathy ◽  
Screening Tool ◽  
External Validation ◽  
Area Under The Curve ◽  
Training Dataset ◽  
Validation Dataset ◽  
Internal Validation ◽  
Diabetic Retinopathy Screening

AIM: To develop a useful diabetic retinopathy (DR) screening tool for patients with type 2 diabetes mellitus (T2DM). METHODS: A DR prediction model based on the Logistic regression algorithm was established on the development dataset containing 778 samples (randomly assigned to the training dataset and the internal validation dataset at a ratio of 7:3). The generalization capability of the model was assessed using an external validation dataset containing 128 samples. The DR risk calculator was developed through WeChat Developer Tools using JavaScript, which was embedded in the WeChat Mini Program. RESULTS: The model revealed risk factors (duration of diabetes, diabetic nephropathy, and creatinine level) and protective factors (annual DR screening and hyperlipidemia) for DR. In the internal and external validation, the recall ratios of the model were 0.92 and 0.89, respectively, and the area under the curve values were 0.82 and 0.70, respectively. CONCLUSION: The DR screening tool integrates education, risk prediction, and medical advice function, which could help clinicians in conducting DR risk assessments and providing recommendations for ophthalmic referral to increase the DR screening rate among patients with T2DM.

scholarly journals Automatic differentiation of thyroid scintigram by deep convolutional neural network: a dual center study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Pei Yang ◽  
Yong Pi ◽  
Tao He ◽  
Jiangming Sun ◽  
Jianan Wei ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Predictive Value ◽  
Automatic Differentiation ◽  
External Validation ◽  
Deep Convolutional Neural Network ◽  
Image Feature ◽  
Training Dataset ◽  
Internal Validation

Abstract Background 99mTc-pertechnetate thyroid scintigraphy is a valid complementary avenue for evaluating thyroid disease in the clinic, the image feature of thyroid scintigram is relatively simple but the interpretation still has a moderate consistency among physicians. Thus, we aimed to develop an artificial intelligence (AI) system to automatically classify the four patterns of thyroid scintigram. Methods We collected 3087 thyroid scintigrams from center 1 to construct the training dataset (n = 2468) and internal validating dataset (n = 619), and another 302 cases from center 2 as external validating datasets. Four pre-trained neural networks that included ResNet50, DenseNet169, InceptionV3, and InceptionResNetV2 were implemented to construct AI models. The models were trained separately with transfer learning. We evaluated each model’s performance with metrics as following: accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), recall, precision, and F1-score. Results The overall accuracy of four pre-trained neural networks in classifying four common uptake patterns of thyroid scintigrams all exceeded 90%, and the InceptionV3 stands out from others. It reached the highest performance with an overall accuracy of 92.73% for internal validation and 87.75% for external validation, respectively. As for each category of thyroid scintigrams, the area under the receiver operator characteristic curve (AUC) was 0.986 for ‘diffusely increased,’ 0.997 for ‘diffusely decreased,’ 0.998 for ‘focal increased,’ and 0.945 for ‘heterogeneous uptake’ in internal validation, respectively. Accordingly, the corresponding performances also obtained an ideal result of 0.939, 1.000, 0.974, and 0.915 in external validation, respectively. Conclusions Deep convolutional neural network-based AI model represented considerable performance in the classification of thyroid scintigrams, which may help physicians improve the interpretation of thyroid scintigrams more consistently and efficiently.

scholarly journals Discovery of Highly Polymorphic Organic Materials: A New Machine Learning Approach

2019 ◽  
Author(s):  
Zied Hosni ◽  
Annalisa Riccardi ◽  
Stephanie Yerdelen ◽  
Alan R. G. Martin ◽  
Deborah Bowering ◽  
...  
Keyword(s):  
Machine Learning ◽  
Structure Prediction ◽  
External Validation ◽  
New Drugs ◽  
Training Dataset ◽  
Validation Dataset ◽  
Machine Learning Classification ◽  
Novel Approach ◽  
Physical Form ◽  
Machine Learning Approach

<div><div><p>Polymorphism is the capacity of a molecule to adopt different conformations or molecular packing arrangements in the solid state. This is a key property to control during pharmaceutical manufacturing because it can impact a range of properties including stability and solubility. In this study, a novel approach based on machine learning classification methods is used to predict the likelihood for an organic compound to crystallise in multiple forms. A training dataset of drug-like molecules was curated from the Cambridge Structural Database (CSD) and filtered according to entries in the Drug Bank database. The number of separate forms in the CSD for each molecule was recorded. A metaclassifier was trained using this dataset to predict the expected number of crystalline forms from the compound descriptors. This approach was used to estimate the number of crystallographic forms for an external validation dataset. These results suggest this novel methodology can be used to predict the extent of polymorphism of new drugs or not-yet experimentally screened molecules. This promising method complements expensive ab initio methods for crystal structure prediction and as integral to experimental physical form screening, may identify systems that with unexplored potential.</p> </div> </div>

scholarly journals A real-world demonstration of machine learning generalizability in the detection of intracranial hemorrhage on head computerized tomography

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hojjat Salehinejad ◽  
Jumpei Kitamura ◽  
Noah Ditkofsky ◽  
Amy Lin ◽  
Aditya Bharatha ◽  
...  
Keyword(s):  
Machine Learning ◽  
Medical Imaging ◽  
Intracranial Hemorrhage ◽  
Real World ◽  
External Validation ◽  
Model Performance ◽  
Training Dataset ◽  
Validation Dataset ◽  
Great Promise ◽  
Clinical Environments

AbstractMachine learning (ML) holds great promise in transforming healthcare. While published studies have shown the utility of ML models in interpreting medical imaging examinations, these are often evaluated under laboratory settings. The importance of real world evaluation is best illustrated by case studies that have documented successes and failures in the translation of these models into clinical environments. A key prerequisite for the clinical adoption of these technologies is demonstrating generalizable ML model performance under real world circumstances. The purpose of this study was to demonstrate that ML model generalizability is achievable in medical imaging with the detection of intracranial hemorrhage (ICH) on non-contrast computed tomography (CT) scans serving as the use case. An ML model was trained using 21,784 scans from the RSNA Intracranial Hemorrhage CT dataset while generalizability was evaluated using an external validation dataset obtained from our busy trauma and neurosurgical center. This real world external validation dataset consisted of every unenhanced head CT scan (n = 5965) performed in our emergency department in 2019 without exclusion. The model demonstrated an AUC of 98.4%, sensitivity of 98.8%, and specificity of 98.0%, on the test dataset. On external validation, the model demonstrated an AUC of 95.4%, sensitivity of 91.3%, and specificity of 94.1%. Evaluating the ML model using a real world external validation dataset that is temporally and geographically distinct from the training dataset indicates that ML generalizability is achievable in medical imaging applications.

scholarly journals Deep Learning Model for Screening Sepsis Using Electrocardiography

2021 ◽  
Author(s):  
Joon-myoung Kwon ◽  
Ye Rang Lee ◽  
Min-Seung Jung ◽  
Yoon-Ji Lee ◽  
Yong-Yeon Jo ◽  
...  
Keyword(s):  
Septic Shock ◽  
Deep Learning ◽  
Characteristic Curve ◽  
External Validation ◽  
Medical Emergency ◽  
Validation Dataset ◽  
Internal Validation ◽  
Significant Difference ◽  
Life Threatening ◽  
Sepsis And Septic Shock

Abstract Background: Sepsis is a life-threatening organ dysfunction and is a major healthcare burden worldwide. Although sepsis is a medical emergency that requires immediate management, it is difficult to screen the occurrence of sepsis. In this study, we propose an artificial intelligence based on deep learning-based model (DLM) for screening sepsis using electrocardiography (ECG).Methods: This retrospective cohort study included 46,017 patients who admitted to two hospitals. 1,548 and 639 patients underwent sepsis and septic shock. The DLM was developed using 73,727 ECGs of 18,142 patients and internal validation was conducted using 7,774 ECGs of 7,774 patients. Furthermore, we conducted an external validation with 20,101 ECGs of 20,101 patients from another hospital to verify the applicability of the DLM across centers.Results: During the internal and external validation, the area under the receiver operating characteristic curve (AUC) of an DLM using 12-lead ECG for screening sepsis were 0.901 (95% confidence interval 0.882–0.920) and 0.863 (0.846–0.879), respectively. During internal and external validation, AUC of an DLM for detecting septic shock were 0.906 (95% CI = 0.877–0.936) and 0.899 (95% CI = 0.872–0.925), respectively. The AUC of the DLM for detecting sepsis using 6-lead and single-lead ECGs were 0.845–0.882. A sensitivity map showed that the QRS complex and T wave was associated with sepsis. Subgroup analysis was conducted using ECGs from 4,609 patients who admitted with infectious disease, The AUC of the DLM for predicting in-hospital mortality was 0.817 (0.793–0.840). There was a significant difference in the prediction score of DLM using ECG according to the presence of infection in the validation dataset (0.277 vs 0.574, p<0.001), including severe acute respiratory syndrome coronavirus 2 (0.260 vs 0.725, p=0.018).Conclusions: The DLM demonstrated reasonable performance for screening sepsis using 12-, 6-, and single-lead ECG. The results suggest that sepsis can be screened using not only conventional ECG devices, but also diverse life-type ECG machine employing the DLM, thereby preventing irreversible disease progression and mortality.

scholarly journals Predicting an Unfavourable Course in Patients Hospitalised for COVID-19: The PREDICT-COVID Study

2020 ◽  
Author(s):  
Michel Ducher ◽  
Christelle Elias ◽  
Nans Florens ◽  
Philippe Vanhems ◽  
jean pierre fauvel
Keyword(s):  
Roc Curve ◽  
Primary Outcome ◽  
External Validation ◽  
Clinical Decision ◽  
Median Number ◽  
Validation Dataset ◽  
Prediction Tool ◽  
University Hospitals ◽  
Clinical Tool ◽  
Internal Validation

Abstract Background. Clinical decision tools that have been proposed to predict the clinical course of patients admitted to hospital with COVID-19 are poorly presented and are at high risk of selection bias. The aim of the study was to propose a prediction clinical tool to predict an unfavourable outcome at the admission of a SARS-CoV2 infected patient that was carefully developed using a large learning database and that was developed from models derived from artificial intelligence.Methods. The PREDICT-COVID study is a post hoc analysis of the Noso-Cor study, a multicenter prospective, observational study. All patients infected by SARS-CoV2 hospitalized in one of the 11 Lyon-University hospitals since 8-March-2020 have been included. The PREDICT-COVID database was split in two separate datasets: the learning dataset (80%) was used for the development of the model and the validation dataset (20%) for internal validation. The primary composite outcome was the need for mechanical ventilation or admission into an intensive care unit, or death within 21 days of admission.Results. Data from 823 patients were analysed: age 70.6±16·9 years; body mass index 26.7±5·4 kg/m2 and median number of comorbidities was 2. Out of the 44 recorded variables, 11 that were the most linked to the primary outcome criteria were retained to develop the optimised risk prediction tool. At admission the 5 most informative predictors were, in descending order: C-Reactive Protein, neutrophil-to-lymphocyte ratio, aspartate transaminase, shortness of breath, and prothrombin time. The ten-fold cross validation of the optimised model had an area under the ROC curve of 0.76±0.06. The performance of the developed Bayesian model to predict the primary outcome of the validation dataset had a mean area under the ROC curve of 0.78, sensitivity of 60%, and specificity of 77%.Conclusions. The proposed optimised prediction tool that uses 11 routinely determined variables to predict an unfavourable course at admission for COVID-19 had satisfactory performance. For an external validation, the PREDICT-COVID prediction tool is available online at: https://www.hed.cc/?a=covid&n=NETCRIT21J.netaTrial registration: The Noso-Cor study was registered on ClinicalTrials (NCT04290780). The present analysis was registered on ClinicalTrials (NCT04412031) the June 2, 2020.

scholarly journals The Comparative Study for Predicting Disease Outbreak

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Anifatul Faricha ◽  
M. Achirul Nanda ◽  
Siti Maghfirotul Ulyah ◽  
Ni'matut Tamimah ◽  
Enny Indasyah ◽  
...  
Keyword(s):  
Predictive Modeling ◽  
External Validation ◽  
Gaussian Process Regression ◽  
Disease Outbreak ◽  
Training Dataset ◽  
Validation Dataset ◽  
Support Vector ◽  
Training Process ◽  
Validation Process ◽  
Death Cases

To know the prediction of disease outbreak, proper predictive modeling is required to represent the dataset. This study presents the comparative predictive modeling for predicting disease outbreak using two models i.e., optimizable support vector machine (SVM) and optimizable gaussian process regression (GPR). The dataset used in this study contains three cases i.e., positive cases, recovered cases, and death cases. The dataset at each case is divided into training dataset for the training process and external validation dataset for the validation process. Based on the training process and validation process, the root mean square error (RMSE) at positive cases, recovered cases, and death cases using optimizable GPR is substantially more effective for prediction than the optimizable SVM. According to the result performance, by applying optimizable GPR, the training process has the average RMSE of 19.54 and the validation process has the average RMSE of 15.85.

scholarly journals Predicting Sex from Retinal Fundus Photographs Using Automated Deep Learning

2021 ◽  
Author(s):  
Edward Korot ◽  
Nikolas Pontikos ◽  
Xiaoxuan Liu ◽  
Siegfried K Wagner ◽  
Livia Faes ◽  
...  
Keyword(s):  
Deep Learning ◽  
Characteristic Curve ◽  
External Validation ◽  
Model Development ◽  
Model Performance ◽  
Validation Dataset ◽  
Internal Validation ◽  
Fundus Photographs ◽  
The Uk ◽  
Retinal Fundus

Abstract Deep learning may transform health care, but model development has largely been dependent on availability of advanced technical expertise. Herein we present the development of a deep learning model by clinicians without coding, which predicts reported sex from retinal fundus photographs. A model was trained on 84,743 retinal fundus photos from the UK Biobank dataset. External validation was performed on 252 fundus photos from a tertiary ophthalmic referral center. For internal validation, the area under the receiver operating characteristic curve (AUROC) of the code free deep learning (CFDL) model was 0.93. Sensitivity, specificity, positive predictive value (PPV) and accuracy (ACC) were 88.8%, 83.6%, 87.3% and 86.5%, and for external validation were 83.9%, 72.2%, 78.2% and 78.6% respectively. Clinicians are currently unaware of distinct retinal feature variations between males and females, highlighting the importance of model explainability for this task. The model performed significantly worse when foveal pathology was present in the external validation dataset, ACC: 69.4%, compared to 85.4% in healthy eyes, suggesting the fovea is a salient region for model performance OR (95% CI): 0.36 (0.19, 0.70) p = 0.0022. Automated machine learning (AutoML) may enable clinician-driven automated discovery of novel insights and disease biomarkers.

scholarly journals Construction of a survival prediction model for high-and low -grade UTUC after tumor resection based on “SEER database”: a multicenter study

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Mengmeng Wang ◽  
Xin Ren ◽  
Ge Wang ◽  
Xiaomin Sun ◽  
Shifeng Tang ◽  
...  
Keyword(s):  
Tumor Resection ◽  
Training Dataset ◽  
Validation Dataset ◽  
Survival Prediction ◽  
Low Grade ◽  
Seer Database ◽  
Discriminative Ability ◽  
Internal Validation ◽  
The Difference ◽  
Good Calibration

Abstract Background There are differences in survival between high-and low-grade Upper Tract Urothelial Carcinoma (UTUC). Our study aimed to develop a nomogram to predict overall survival (OS) of patients with high- and low-grade UTUC after tumor resection, and to explore the difference between high- and low-grade patients. Methods Patients confirmed to have UTUC between 2004 and 2015 were selected from the Surveillance, Epidemiology and End Results (SEER) database. The UTUCs were identified and classified as high- and low-grade, and 1-, 3- and 5-year nomograms were established. The nomogram was then validated using the Chinese multicenter dataset (patients diagnosed in Shandong, China between January 2010 and October 2020). Results In the high-grade UTUC patients, nine important factors related to survival after tumor resection were identified to construct nomogram. The C index of training dataset was 0.740 (95% confidence interval [CI]: 0.727–0.754), showing good calibration. The C index of internal validation dataset was 0.729(95% CI:0.707–0.750). On the other hand, Two independent predictors were identified to construct nomogram of low-grade UTUC. The C index was 0.714 (95% CI: 0.671–0.758) for the training set,0.731(95% CI:0.670–0.791) for the internal validation dataset. Encouragingly, the nomogram was clinically useful and had a good discriminative ability to identify patients at high risk. Conclusion We constructed a nomogram and a corresponding risk classification system predicting the OS of patients with an initial diagnosis of high-and low-grade UTUC.

scholarly journals Easily created prediction model using deep learning software (Prediction One, Sony Network Communications Inc.) for subarachnoid hemorrhage outcomes from small dataset at admission

2020 ◽  
Vol 11 ◽  
pp. 374
Author(s):  
Masahito Katsuki ◽  
Yukinari Kakizawa ◽  
Akihiro Nishikawa ◽  
Yasunaga Yamamoto ◽  
Toshiya Uchiyama
Keyword(s):  
Deep Learning ◽  
Subarachnoid Hemorrhage ◽  
Prediction Model ◽  
Prediction Models ◽  
External Validation ◽  
Training Dataset ◽  
Validation Dataset ◽  
Good Prediction ◽  
Network Communications ◽  
Learning Software

Background: Reliable prediction models of subarachnoid hemorrhage (SAH) outcomes are needed for decision-making of the treatment. SAFIRE score using only four variables is a good prediction scoring system. However, making such prediction models needs a large number of samples and time-consuming statistical analysis. Deep learning (DL), one of the artificial intelligence, is attractive, but there were no reports on prediction models for SAH outcomes using DL. We herein made a prediction model using DL software, Prediction One (Sony Network Communications Inc., Tokyo, Japan) and compared it to SAFIRE score. Methods: We used 153 consecutive aneurysmal SAH patients data in our hospital between 2012 and 2019. Modified Rankin Scale (mRS) 0–3 at 6 months was defined as a favorable outcome. We randomly divided them into 102 patients training dataset and 51 patients external validation dataset. Prediction one made the prediction model using the training dataset with internal cross-validation. We used both the created model and SAFIRE score to predict the outcomes using the external validation set. The areas under the curve (AUCs) were compared. Results: The model made by Prediction One using 28 variables had AUC of 0.848, and its AUC for the validation dataset was 0.953 (95%CI 0.900–1.000). AUCs calculated using SAFIRE score were 0.875 for the training dataset and 0.960 for the validation dataset, respectively. Conclusion: We easily and quickly made prediction models using Prediction One, even with a small single-center dataset. The accuracy of the model was not so inferior to those of previous statistically calculated prediction models.

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