scholarly journals Using deep learning to predict the outcome of live birth from more than 10,000 embryo data

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Bo Huang ◽  
Shunyuan Zheng ◽  
Bingxin Ma ◽  
Yongle Yang ◽  
Shengping Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Birth Outcomes ◽  
Live Birth ◽  
Cross Validation ◽  
Birth Outcome ◽  
Time Lapse ◽  
Learning Model ◽  
Learning Rate ◽  
End Point ◽  
Deep Learning Model

Abstract Background Recently, the combination of deep learning and time-lapse imaging provides an objective, standard and scientific solution for embryo selection. However, the reported studies were based on blastocyst formation or clinical pregnancy as the end point. To the best of our knowledge, there is no predictive model that uses the outcome of live birth as the predictive end point. Can a deep learning model predict the probability of live birth from time-lapse system? Methods This study retrospectively analyzed the time-lapse data and live birth outcomes of embryos samples from January 2018 to November 2019. We used the SGD optimizer with an initial learning rate of 0.025 and cosine learning rate reduction strategy. The network is randomly initialized and trained for 200 epochs from scratch. The model is quantitively evaluated over a hold-out test and a 5-fold cross-validation by the average area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Results The deep learning model was able to predict live birth outcomes from time-lapse images with an AUC of 0.968 in 5-fold stratified cross-validation. Conclusions This research reported a deep learning model that predicts the live birth outcome of a single blastocyst transfer. This efficient model for predicting the outcome of live births can automatically analyze the time-lapse images of the patient’s embryos without the need for manual embryo annotation and evaluation, and then give a live birth prediction score for each embryo, and sort the embryos by the predicted value.

scholarly journals NIMG-08. PREDICTION OF LOWER-GRADE GLIOMA MOLECULAR SUBTYPES USING DEEP LEARNING

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii148-ii148
Author(s):  
Yoshihiro Muragaki ◽  
Yutaka Matsui ◽  
Takashi Maruyama ◽  
Masayuki Nitta ◽  
Taiichi Saito ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cross Validation ◽  
Molecular Subtype ◽  
Learning Model ◽  
Group Classification ◽  
Training Dataset ◽  
Lower Grade ◽  
Test Dataset ◽  
Ct Data ◽  
Deep Learning Model

Abstract INTRODUCTION It is useful to know the molecular subtype of lower-grade gliomas (LGG) when deciding on a treatment strategy. This study aims to diagnose this preoperatively. METHODS A deep learning model was developed to predict the 3-group molecular subtype using multimodal data including magnetic resonance imaging (MRI), positron emission tomography (PET), and computed tomography (CT). The performance was evaluated using leave-one-out cross validation with a dataset containing information from 217 LGG patients. RESULTS The model performed best when the dataset contained MRI, PET, and CT data. The model could predict the molecular subtype with an accuracy of 96.6% for the training dataset and 68.7% for the test dataset. The model achieved test accuracies of 58.5%, 60.4%, and 59.4% when the dataset contained only MRI, MRI and PET, and MRI and CT data, respectively. The conventional method used to predict mutations in the isocitrate dehydrogenase (IDH) gene and the codeletion of chromosome arms 1p and 19q (1p/19q) sequentially had an overall accuracy of 65.9%. This is 2.8 percent point lower than the proposed method, which predicts the 3-group molecular subtype directly. CONCLUSIONS AND FUTURE PERSPECTIVE A deep learning model was developed to diagnose the molecular subtype preoperatively based on multi-modality data in order to predict the 3-group classification directly. Cross-validation showed that the proposed model had an overall accuracy of 68.7% for the test dataset. This is the first model to double the expected value for a 3-group classification problem, when predicting the LGG molecular subtype. We plan to apply the techniques of heat map and/or segmentation for an increase in prediction accuracy.

scholarly journals Multi- class classification of breast cancer abnormalities using Deep Convolutional Neural Network (CNN)

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256500
Author(s):  
Maleika Heenaye-Mamode Khan ◽  
Nazmeen Boodoo-Jahangeer ◽  
Wasiimah Dullull ◽  
Shaista Nathire ◽  
Xiaohong Gao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Deep Learning ◽  
Early Detection ◽  
Convolutional Neural Network ◽  
Learning Model ◽  
Learning Rate ◽  
Deep Convolutional Neural Network ◽  
Deep Learning Model

The real cause of breast cancer is very challenging to determine and therefore early detection of the disease is necessary for reducing the death rate due to risks of breast cancer. Early detection of cancer boosts increasing the survival chance up to 8%. Primarily, breast images emanating from mammograms, X-Rays or MRI are analyzed by radiologists to detect abnormalities. However, even experienced radiologists face problems in identifying features like micro-calcifications, lumps and masses, leading to high false positive and high false negative. Recent advancement in image processing and deep learning create some hopes in devising more enhanced applications that can be used for the early detection of breast cancer. In this work, we have developed a Deep Convolutional Neural Network (CNN) to segment and classify the various types of breast abnormalities, such as calcifications, masses, asymmetry and carcinomas, unlike existing research work, which mainly classified the cancer into benign and malignant, leading to improved disease management. Firstly, a transfer learning was carried out on our dataset using the pre-trained model ResNet50. Along similar lines, we have developed an enhanced deep learning model, in which learning rate is considered as one of the most important attributes while training the neural network. The learning rate is set adaptively in our proposed model based on changes in error curves during the learning process involved. The proposed deep learning model has achieved a performance of 88% in the classification of these four types of breast cancer abnormalities such as, masses, calcifications, carcinomas and asymmetry mammograms.

Time-lapse seismic data inversion for estimating reservoir parametersusing deep learning

2021 ◽  
pp. 1-91
Author(s):  
Harpreet Kaur ◽  
Zhi Zhong ◽  
Alexander Sun ◽  
Sergey Fomel
Keyword(s):  
Carbon Dioxide ◽  
Deep Learning ◽  
Time Lapse ◽  
Learning Model ◽  
Training Phase ◽  
Long Term Storage ◽  
Porosity And Permeability ◽  
Deep Learning Model ◽  
Term Storage

Geological carbon sequestration involves the injection of captured carbon dioxide (CO2) into sub-surface formations for long-term storage. The movement and fate of the injected CO2 plume is ofgreat concern to regulators as monitoring helps to identify potential leakage zones and determinesthe possibility of safe long-term storage. To address this concern, we design a deep learning frame-work for carbon dioxide (CO2) saturation monitoring to determine the geological controls on thestorage of the injected CO2. We use different combinations of porosities and permeabilities for agiven reservoir to generate saturation and velocity models. We train the deep learning model with afew time-lapse seismic images and their corresponding changes in saturation values for a particular CO2 injection site. The deep learning model learns the mapping from the change in the time-lapseseismic response to the change in CO2 saturation during the training phase. We then apply thetrained model to data sets comprising different time-lapse seismic image slices (corresponding todifferent time instances) generated using different porosity and permeability distributions that arenot part of the training to estimate the CO2 saturation values along with the plume extent. Theproposed algorithm provides a deep learning assisted framework for the direct estimation of CO2 saturation values and plume migration in heterogeneous formations using the time-lapse seismicdata. The proposed method improves the efficiency of time-lapse inversion by streamlining thelarge number of intermediate steps in the conventional time-lapse inversion workflow. This method also helps to incorporate the geological uncertainty for a given reservoir by accounting for the statis-tical distribution of porosity and permeability during the training phase. Tests on different examplesverify the effectiveness of the proposed approach

O-086 End-to-end deep learning for recognition of ploidy status using time-lapse videos

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
C I Lee ◽  
Y R Su ◽  
C H Chen ◽  
T A Chang ◽  
E E S Kuo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Optical Flow ◽  
Time Lapse ◽  
Learning Model ◽  
Learning Models ◽  
Group 2 ◽  
Deep Learning Model ◽  
Ploidy Status ◽  
Time Lapse Video ◽  
Group 1

Abstract Study question Our Retrospective study is to investigate an end-to-end deep learning model in identifying ploidy status through raw time-lapse video. Summary answer Our deep learning model demonstrates a proof of concept and potential in recognizing the ploidy status. What is known already Since the time-lapse system has been introduced into the IVF lab, the relationship between morphogenetic and ploidy status has been often discussed. However, the result has not yet reached a united conclusion due to some limitations such as human labeling. Besides the statistical approach, deep learning models have been utilized for ploidy prediction. As such approaches are single image-based, the performance remains unpromising as previous statistical-based research. Therefore, in order to move further toward clinical application, better research design and approach are needed. Study design, size, duration A retrospective analysis of the time-lapse videos and chromosomal status from 690 biopsied blastocysts cultured in a time-lapse incubator (EmbryoScope+, Vitrolife) between January 2017 and August 2018 in the Lee Women’s Hospital were assessed. The ploidy status of the blastocyst was derived from the PGT-A using high-resolution next-generation sequencing (hr-NGS). Embryo videos were obtained after normal fertilization through the intracytoplasmic sperm injection or conventional insemination. Participants/materials, setting, methods By randomly dividing the data into 80% and 20%, we developed our deep learning model based on Two-Stream Inflated 3D ConvNets(I3D) network. This model was trained by the 80% time-lapse videos and the PGT-A result. The remaining 20% has been tested by feeding the time-lapse video as input and the PGT-A prediction as output. Ploidy status was classified as Group 1 (aneuploidy) and Group 2 (euploidy and mosaicism). Main results and the role of chance Time-lapse videos were divided into 3-time partitions: day 1, day 1 to 3, and day 1 to 5. Deep learning models have been fed by RGB and optical flow. Combining 3 different time partitions with RGB, optical flow, and fused result from RGB and optical flow, we received nine sets of test results. According to the results, the longest time partition with the fusion method has the highest AUC result as 0.74, which appeared higher than the other eight experimental settings with a maximum increase of 0.17. Limitations, reasons for caution The present study is retrospective and future prospective research would help us to identify more key factors and improve this model. In addition, expanding sample size combined with cross-centered validation will also be considered in our future approach. Wider implications of the findings Group 1 and Group 2 approach provided deselection of aneuploidy embryos, while future deep learning approaches toward high mosaicism, low mosaicism, and euploidy will be needed, in order to provide a better clinical application. Trial registration number CS18082

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

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