A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models

Visual morphology assessment is routinely used for evaluating of embryo quality and selecting human blastocysts for transfer after in vitro fertilization (IVF). However, the assessment produces different results between embryologists and as a result, the success rate of IVF remains low. To overcome uncertainties in embryo quality, multiple embryos are often implanted resulting in undesired multiple pregnancies and complications. Unlike in other imaging fields, human embryology and IVF have not yet leveraged artificial intelligence (AI) for unbiased, automated embryo assessment. We postulated that an AI approach trained on thousands of embryos can reliably predict embryo quality without human intervention. We implemented an AI approach based on deep neural networks (DNNs) to select highest quality embryos using a large collection of human embryo time-lapse images (about 50,000 images) from a high-volume fertility center in the United States. We developed a framework (STORK) based on Google’s Inception model. STORK predicts blastocyst quality with an AUC of >0.98 and generalizes well to images from other clinics outside the US and outperforms individual embryologists. Using clinical data for 2182 embryos, we created a decision tree to integrate embryo quality and patient age to identify scenarios associated with pregnancy likelihood. Our analysis shows that the chance of pregnancy based on individual embryos varies from 13.8% (age ≥41 and poor-quality) to 66.3% (age <37 and good-quality) depending on automated blastocyst quality assessment and patient age. In conclusion, our AI-driven approach provides a reproducible way to assess embryo quality and uncovers new, potentially personalized strategies to select embryos.

/pdf/deep-learning-enables-robust-assessment-and-selection-of-zky4c3ge3q.pdf

Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization.

To determine whether erythromycin is non-inferior to metoclopramide in facilitating post-pyloric placement of self-propelled spiral nasoenteric tubes (NETs) in critically ill patients. A prospective, multicenter, open-label, parallel, and non-inferiority randomized controlled trial was conducted comparing erythromycin with metoclopramide in facilitating post-pyloric placement of spiral NETs in critically ill patients admitted to intensive care units (ICUs) of eight tertiary hospitals in China. The primary outcome was procedure success defined as post-pyloric placement (spiral NETs reached the first portion of the duodenum or beyond confirmed by abdominal radiography 24 h after tube insertion). A total of 5688 patients were admitted to the ICUs. Of these, in 355 patients there was a plan to insert a nasoenteric feeding tube, of whom 332 were randomized, with 167 patients assigned to the erythromycin group and 165 patients assigned to the metoclopramide group. The success rate of post-pyloric placement was 57.5% (96/167) in the erythromycin group, as compared with 50.3% (83/165) in the metoclopramide group (a difference of 7.2%, 95% CI − 3.5% to 17.9%), in the intention-to-treat analysis, not including the prespecified margin of − 10% for non-inferiority. The success rates of post-D1 (reaching the second portion of the duodenum or beyond), post-D2 (reaching the third portion of the duodenum or beyond), post-D3 (reaching the fourth portion of the duodenum or beyond), and proximal jejunum placement and the incidence of any adverse events were not significantly different between the groups. Erythromycin is non-inferior to metoclopramide in facilitating post-pyloric placement of spiral NETs in critically ill patients. The success rates of post-D1, post-D2, post-D3, and proximal jejunum placement were not significantly different.

https://link.springer.com/content/pdf/10.1007/s00134-018-5466-4.pdf

Erythromycin versus metoclopramide for post-pyloric spiral nasoenteric tube placement: a randomized non-inferiority trial.

FPGA-based implementation of classification techniques: A survey

Morphology assessment has become the standard method for evaluation of embryo quality and selecting human blastocysts for transfer in in vitro fertilization (IVF). This process is highly subjective for some embryos and thus prone to human bias. As a result, morphological assessment results may vary extensively between embryologists and in some cases may fail to accurately predict embryo implantation and live birth potential. Here we postulated that an artificial intelligence (AI) approach trained on thousands of embryos can reliably predict embryo quality without human intervention. To test this hypothesis, we implemented an AI approach based on deep neural networks (DNNs). Our approach called STORK accurately predicts the morphological quality of blastocysts based on raw digital images of embryos with 98% accuracy. These results indicate that a DNN can automatically and accurately grade embryos based on raw images. Using clinical data for 2,182 embryos, we then created a decision tree that integrates clinical parameters such as embryo quality and patient age to identify scenarios associated with increased or decreased pregnancy chance. This IVF data-driven analysis shows that the chance of pregnancy varies from 13.8% to 66.3%. In conclusion, our AI-driven approach provides a novel way to assess embryo quality and uncovers new, potentially personalized strategies to select embryos with an improved likelihood of pregnancy outcome.

/pdf/robust-automated-assessment-of-human-blastocyst-quality-56dnpz76gv.pdf

Robust Automated Assessment of Human Blastocyst Quality using Deep Learning

Background Despite the use of prokinetic agents, the overall success rate for postpyloric placement via a self-propelled spiral nasoenteric tube is quite low. Methods This retrospective study was conducted in the intensive care units of 11 university hospitals from 2006 to 2016 among adult patients who underwent self-propelled spiral nasoenteric tube insertion. Success was defined as postpyloric nasoenteric tube placement confirmed by abdominal x-ray scan 24 hours after tube insertion. Chi-square automatic interaction detection (CHAID), simple classification and regression trees (SimpleCart), and J48 methodologies were used to develop decision tree models, and multiple logistic regression (LR) methodology was used to develop an LR model for predicting successful postpyloric nasoenteric tube placement. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of these models. Results Successful postpyloric nasoenteric tube placement was confirmed in 427 of 939 patients enrolled. For predicting successful postpyloric nasoenteric tube placement, the performance of the 3 decision trees was similar in terms of the AUCs: 0.715 for the CHAID model, 0.682 for the SimpleCart model, and 0.671 for the J48 model. The AUC of the LR model was 0.729, which outperformed the J48 model. Conclusion Both the CHAID and LR models achieved an acceptable discrimination for predicting successful postpyloric nasoenteric tube placement and were useful for intensivists in the setting of self-propelled spiral nasoenteric tube insertion.

Establishing Decision Trees for Predicting Successful Postpyloric Nasoenteric Tube Placement in Critically Ill Patients.

Development and validation of a nomogram for predicting self-propelled postpyloric placement of spiral nasoenteric tube in the critically ill: Mixed retrospective and prospective cohort study.

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Establishing Decision Trees for Predicting Successful Postpyloric Nasoenteric Tube Placement in Critically Ill Patients.

Development and validation of a nomogram for predicting self-propelled postpyloric placement of spiral nasoenteric tube in the critically ill: Mixed retrospective and prospective cohort study.