Predicting adverse perinatal outcomes among gestational diabetes complicated pregnancies using neural network algorithm.

OBJECTIVE: The primary aim of this study is to utilize a neural network model to predict adverse neonatal outcomes in pregnancies complicated by gestational diabetes (GDM). DESIGN: Our model, based on XGBoost, was implemented using Python 3.6 with the Keras framework built on TensorFlow by Google. We sourced data from medical records of GDM-diagnosed individuals who delivered at our tertiary medical center between 2012 and 2016. The model included simple pregnancy parameters, maternal age, body mass index (BMI), parity, gravity, results of oral glucose tests, treatment modality, and glycemic control. The composite neonatal adverse outcomes defined as one of the following: large or small for gestational age, shoulder dystocia, fetal umbilical pH less than 7.2, neonatal intensive care unit (NICU) admission, respiratory distress syndrome (RDS), hyperbilirubinemia, or polycythemia. For the machine training phase, 70% of the cohort was randomly chosen. Each sample in this set consisted of baseline parameters and the composite outcome. The remaining samples were then employed to assess the accuracy of our model. RESULTS: The study encompassed a total of 452 participants. The composite adverse outcome occurred in 29% of cases. Our model exhibited prediction accuracies of 82% at the time of GDM diagnosis and 91% at delivery. The factors most contributing to the prediction model were maternal age, pre-pregnancy BMI, and the results of the single 3-h 100 g oral glucose tolerance test. CONCLUSION: Our advanced neural network algorithm has significant potential in predicting adverse neonatal outcomes in GDM-diagnosed individuals.

Prediction of oocyte maturation rate in the GnRH antagonist flexible IVF protocol using a novel machine learning algorithm - A retrospective study.

Oocyte maturation is affected by various patient and cycle parameters and has a key effect on treatment outcome. A prediction model for oocyte maturation rate formulated by using machine learning and neural network algorithms has not yet been described. A retrospective cohort study that included all women aged ≤ 38 years who underwent their first IVF treatment using a flexible GnRH antagonist protocol in a single tertiary hospital between 2010 and 2015. 462 patients met the inclusion criteria. Median maturation rate was approximately 80%. Baseline characteristics and treatment parameters of cycles with high oocyte maturation rate (≥80%, n = 236) were compared to cycles with low oocyte maturation rate (<80%, n = 226). We used an XGBoost algorithm that fits the training data using decision trees and rates factors according to their influence on the prediction. For the machine training phase, 80% of the cohort was randomly selected, while rest of the samples were used to evaluate our model's accuracy. We demonstrated an accuracy rate of 75% in predicting high oocyte maturation rate in GnRH antagonist cycles. Our model showed an operating characteristic curve with AUC of 0.78 (95% CI 0.73-0.82). The most predictive parameters were peak estradiol level on trigger day, estradiol level on antagonist initiation day, average dose of gonadotropins per day and progesterone level on trigger day. A state-of-the-art machine learning algorithm presented promising ability to predict oocyte maturation rate in the first GnRH antagonist flexible protocol using simple parameters before final trigger for ovulation. A prospective study to evaluate this model is needed.

Prediction of endometrial cancer recurrence by using a novel machine learning algorithm: An Israeli gynecologic oncology group study.

OBJECTIVES: Endometrial cancer is the most common gynecologic malignancy in developed countries. The overall risk of recurrence is associated with traditional risk factors. METHODS: Machine learning was used to predict recurrence among women who were diagnosed and treated for endometrial cancer between 2002 and 2012 at elven university-affiliated centers. The median follow-up time was 5 years. The following data were retrieved from the medical records and fed into the algorithm: age, chronic metabolic diseases, family and personal cancer history, hormone replacement therapy use, endometrial thickness, uterine polyp presence, complete blood count results, albumin, Ca-125 level, surgical staging, histology, depth of myometrial invasion, LVSI, grade, pelvic washing cytology, and adjuvant treatment. We used XGBoost algorithm, which fits the training data using decision trees, and can also rate the factors according to their influence on the prediction. RESULTS: 1935 women were identified of whom 325 had recurrent disease. On the randomly picked samples, the specificity was 55% and the sensitivity was 98%. Our model showed an operating characteristic curve with AUC of 0.84. CONCLUSIONS: A machine learning algorithm presented promising ability to predict recurrence of endometrial cancer. The algorithm provides an opportunity to identify at-risk patients who may benefit from adjuvant therapy, tighter surveillance, and early intervention.

Feasibility of retroreflective transdermal optical wireless communication.

There is an increasing demand for transdermal high-data-rate communication for use with in-body devices, such as pacemakers, smart prostheses, neural signals processors at the brain interface, and cameras acting as artificial eyes as well as for collecting signals generated within the human body. Prominent requirements of these communication systems include (1) wireless modality, (2) noise immunity and (3) ultra-low-power consumption for the in-body device. Today, the common wireless methods for transdermal communication are based on communication at radio frequencies, electrical induction, or acoustic waves. In this paper, we will explore another alternative to these methods--optical wireless communication (OWC)--for which modulated light carries the information. The main advantages of OWC in transdermal communication, by comparison to the other methods, are the high data rates and immunity to external interference availed, which combine to make it a promising technology for next-generation systems. In this paper, we present a mathematical model and experimental results of measurements from direct link and retroreflection link configurations with Gallus gallus domesticus derma as the transdermal channel. The main conclusion from this work is that an OWC link is an attractive communication solution in medical applications. For a modulating retroreflective link to become a competitive solution in comparison with a direct link, low-energy-consumption modulating retroreflectors should be developed.