Obstetrics/Gynecology


A Machine-Learning Approach to the Detection of Fetal Hypoxia during Labor and Delivery

AI Magazine

In this article we focus on detecting hypoxia (or oxygen deprivation), a very serious condition that can arise from different pathologies and can lead to lifelong disability and death. We present a novel approach to hypoxia detection based on recordings of the uterine pressure and fetal heart rate, which are obtained using standard labor monitoring devices. The key idea is to learn models of the fetal response to signals from its environment. Then, we use the parameters of these models as attributes in a binary classification problem. A running count of pathological classifications over several time periods is taken to provide the current label for the fetus.


Teenager Aims To Improve Breast Cancer Diagnosis In Poor Countries

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Abu Qader, 18, came to the U.S. from Afghanistan as a baby. Now a freshman at Cornell University, he has founded a medical technology company with the goal of improving diagnosis of breast cancer in poor countries. Abu Qader, 18, came to the U.S. from Afghanistan as a baby. Now a freshman at Cornell University, he has founded a medical technology company with the goal of improving diagnosis of breast cancer in poor countries. After a family trip to Afghanistan when he was 15, Chicagoan Abu Qader decided he wanted to do something to improve the country's medical care.


Artificial Intelligence Promising for Breast Cancer Metastases Detection

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A deep learning algorithm can detect metastases in sections of lymph nodes from women with breast cancer; and a deep learning system (DLS) has high sensitivity and specificity for identifying diabetic retinopathy, according to two studies published online Dec. 12 in the Journal of the American Medical Association. Babak Ehteshami Bejnordi, from the Radboud University Medical Center in Nijmegen, Netherlands, and colleagues compared the performance of automated deep learning algorithms for detecting metastases in hematoxylin and eosin-stained tissue sections of lymph nodes of women with breast cancer with pathologists' diagnoses in a diagnostic setting. The researchers found that the area under the receiver operating characteristic curve (AUC) ranged from 0.556 to 0.994 for the algorithms. The lesion-level, true-positive fraction achieved for the top-performing algorithm was comparable to that of the pathologist without a time constraint at a mean of 0.0125 false-positives per normal whole-slide image. Daniel Shu Wei Ting, M.D., Ph.D., from the Singapore National Eye Center, and colleagues assessed the performance of a DLS for detecting referable diabetic retinopathy and related eye diseases using 494,661 retinal images.


Artificial intelligence rivals doctors in spotting spread of breast cancer

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Artificial intelligence is just as good at spotting the spread of breast cancer as specialists, a study suggests. Advanced algorithms were as accurate as an experienced pathologist in selecting metastatic tissue samples and did even better than specialists rushing against the clock, the study, the first of its kind, found. While the findings need to be repeated, the "exciting" success of artificial intelligence in interpreting images of human tissue opens a new front in efforts to harness technology to improve diagnostics. Artificial intelligence is becoming routine in interpreting scans such as x-rays, but until now has not been much used in pathology services that analyse biopsies and other tissue samples.


Accuracy of Artificial Intelligence Assessed in CA Diagnosis

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A deep learning algorithm can detect metastases in sections of lymph nodes from women with breast cancer; and a deep learning system (DLS) has high sensitivity and specificity for identifying diabetic retinopathy, according to two studies published online December 12 in the Journal of the American Medical Association. Babak Ehteshami Bejnordi, from the Radboud University Medical Center in Nijmegen, Netherlands, and colleagues compared the performance of automated deep learning algorithms for detecting metastases in hematoxylin and eosin-stained tissue sections of lymph nodes of women with breast cancer with pathologists' diagnoses in a diagnostic setting. The researchers found that the area under the receiver operating characteristic curve (AUC) ranged from 0.556 to 0.994 for the algorithms. The lesion-level, true-positive fraction achieved for the top-performing algorithm was comparable to that of the pathologist without a time constraint at a mean of 0.0125 false-positives per normal whole-slide image. Daniel Shu Wei Ting, MD, PhD, from the Singapore National Eye Center, and colleagues assessed the performance of a DLS for detecting referable diabetic retinopathy and related eye diseases using 494,661 retinal images.


Artificial intelligence to aid IVF treatment: Interview (Includes interview and first-hand account)

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This is in order to give personalized probability of in vitro fertilisation (IVF) success. A secondary aim is to bring more transparency to the success and cost of IVF. Moreover, by using Univfy patients can learn about the costs of IVF treatment and their probability of success after one, two or three IVF cycles. The platform, therefore, also helps couples to make better financial decisions. To discover more about this combination of artificial intelligence and predictive technology, we spoke with the Chief Executive Officer, Dr. Mylene Yao.


Eugenics 2.0: We're at the dawn of choosing embryos by health, height, and more

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Nathan Treff was diagnosed with type 1 diabetes at 24. It's a disease that runs in families, but it has complex causes. More than one gene is involved. And the environment plays a role too. So you don't know who will get it.


Eugenics 2.0: We're at the Dawn of Choosing Embryos by Health, Height, and More

MIT Technology Review

Nathan Treff was diagnosed with type 1 diabetes at 24. It's a disease that runs in families, but it has complex causes. More than one gene is involved. And the environment plays a role too. So you don't know who will get it.


Predicting Breast Cancer Using Apache Spark Machine Learning Logistic Regression

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Then we use another map transformation, which will apply the ParseObs function to transform each Array of Double in the RDD into an Array of Cancer Observation objects. The toDF() method transforms the RDD of Array[[Cancer Observation]] into a Dataframe with the Cancer Observation class schema. Below the data is split into a training data set and a test data set, 70% of the data is used to train the model, and 30% will be used for testing. In this blog post, we showed you how to get started using Apache Spark's machine learning Logistic Regression for classification.


Proposed machine learning-based framework predicts FGR pregnancies with high accuracy

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Using three-dimensional (3D) magnetic resonance imaging (MRI), the research team characterized the shape, volume, morphometry and texture of placentas during pregnancy and, using a novel framework, predicted with high accuracy which pregnancies would be complicated by fetal growth restriction (FGR). The Children's research team acquired 124 fetal scans from 80 pregnancies beginning at the 18th gestational week and continuing through the 39th gestational week. Forty-six women had normal pregnancies and healthy fetuses while 34 women's pregnancies were complicated by FGR, defined by estimated fetal weight that fell below the 10th percentile for gestational age. The proposed machine learning-based framework distinguished healthy pregnancies from FGR pregnancies with 86 percent accuracy and 87 percent specificity.