Obstetrics & Gynecology

Comparative analysis of deep-learning-based bone age estimation between whole lateral cephalometric and the cervical vertebral region in children.

Bone age determination in individuals is important for the diagnosis and treatment of growing children. This study aimed to develop a deep-learning model for bone age estimation using lateral cephalometric radiographs (LCRs) and regions of interest (ROIs) in growing children and evaluate its performance. This retrospective study included 1050 patients aged 4-18 years who underwent LCR and hand-wri...

Ovarian cancer identification technology based on deep learning and second harmonic generation imaging.

Ovarian cancer is among the most common gynecological cancers and the eighth leading cause of cancer-related deaths among women worldwide. Surgery is among the most important options for cancer treatment. During surgery, a biopsy is generally required to screen for lesions; however, traditional case examinations are time consuming and laborious and require extensive experience and knowledge from p...

An automatic deep reinforcement learning bolus calculator for automated insulin delivery systems.

In hybrid automatic insulin delivery (HAID) systems, meal disturbance is compensated by feedforward control, which requires the announcement of the me...

Derivation and validation of the first web-based nomogram to predict the spontaneous pregnancy after reproductive surgery using machine learning models.

OBJECTIVE: Infertility remains a significant global burden over the years. Reproductive surgery is an effective strategy for infertile women. Early pr...

A high hydrophobic moment arginine-rich peptide screened by a machine learning algorithm enhanced ADC antitumor activity.

Cell-penetrating peptides (CPPs) with better biomolecule delivery properties will expand their clinical applications. Using the MLCPP2.0 machine algor...

Acceptability of artificial intelligence for cervical cancer screening in Dschang, Cameroon: a qualitative study on patient perspectives.

BACKGROUND: Cervical cancer is the fourth most frequent cancer among women, with 90% of cervical cancer-related deaths occurring in low- and middle-in...

Automated Segmentation of Lymph Nodes on Neck CT Scans Using Deep Learning.

Early and accurate detection of cervical lymph nodes is essential for the optimal management and staging of patients with head and neck malignancies. ...

Clinical evaluation of an artificial intelligence-assisted cytological system among screening strategies for a cervical cancer high-risk population.

BACKGROUND: Primary cervical cancer screening and treating precancerous lesions are effective ways to prevent cervical cancer. However, the coverage r...

Predicting Future Birth Rates with the Use of an Adaptive Machine Learning Algorithm: A Forecasting Experiment for Scotland.

The total fertility rate is influenced over an extended period of time by shifts in population socioeconomic characteristics and attitudes and values....

Integrated machine learning identifies a cellular senescence-related prognostic model to improve outcomes in uterine corpus endometrial carcinoma.

BACKGROUND: Uterine Corpus Endometrial Carcinoma (UCEC) stands as one of the prevalent malignancies impacting women globally. Given its heterogeneous ...

Predicting lower limb lymphedema after cervical cancer surgery using artificial neural network and decision tree models.

PURPOSE: This study aimed to develop and validate accessible artificial neural network and decision tree models to predict the risk of lower limb lymp...

Machine learning in drug delivery.

For decades, drug delivery scientists have been performing trial-and-error experimentation to manually sample parameter spaces and optimize release pr...

Synergistic Polymer Blending Informs Efficient Terpolymer Design and Machine Learning Discerns Performance Trends for pDNA Delivery.

Cationic polymers offer an alternative to viral vectors in nucleic acid delivery. However, the development of polymer vehicles capable of high transfe...

Machine Learning-Enhanced Extraction of Biomarkers for High-Grade Serous Ovarian Cancer from Proteomics Data.

Comprehensive biomedical proteomic datasets are accumulating exponentially, warranting robust analytics to deconvolute them for identifying novel biol...

Estimating mandibular growth stage based on cervical vertebral maturation in lateral cephalometric radiographs using artificial intelligence.

INTRODUCTION: Determining the right time for orthodontic treatment is one of the most important factors affecting the treatment plan and its outcome. ...

Unveiling the Role of Artificial Intelligence (AI) in Polycystic Ovary Syndrome (PCOS) Diagnosis: A Comprehensive Review.

Polycystic Ovary Syndrome (PCOS) is one of the most widespread endocrine and metabolic disorders affecting women of reproductive age. Major symptoms i...

A machine learning model to predict spontaneous vaginal delivery failure for term nulliparous women: An observational study.

OBJECTIVE: This study aims to construct and evaluate a model to predict spontaneous vaginal delivery (SVD) failure in term nulliparous women based on ...

From plan to delivery: Machine learning based positional accuracy prediction of multi-leaf collimator and estimation of delivery effect in volumetric modulated arc therapy.

PURPOSE: The positional accuracy of MLC is an important element in establishing the exact dosimetry in VMAT. We comprehensively analyzed factors that ...

An improved approach for automated cervical cell segmentation with PointRend.

Regular screening for cervical cancer is one of the best tools to reduce cancer incidence. Automated cell segmentation in screening is an essential ta...

Machine learning predicts the serum PFOA and PFOS levels in pregnant women: Enhancement of fatty acid status on model performance.

Human exposure to per- and polyfluoroalkyl substances (PFASs) has received considerable attention, particularly in pregnant women because of their dra...