Obstetrics & Gynecology

Deep neural network (DNN) modelling for prediction of the mode of delivery.

One of the factors that worry obstetricians the most is the method of delivery. In recent years, the rate of caesarean sections has steadily climbed and now exceeds the threshold advised by medical organizations. Obstetricians typically lack the tools they need to assess whether vaginal delivery or a caesarean delivery is more appropriate. In this work, we suggested a computerized decision-making ...

Development and validation of an ultrasound-based deep learning radiomics nomogram for predicting the malignant risk of ovarian tumours.

BACKGROUND: The timely identification and management of ovarian cancer are critical determinants of patient prognosis. In this study, we developed and validated a deep learning radiomics nomogram (DLR_Nomogram) based on ultrasound (US) imaging to accurately predict the malignant risk of ovarian tumours and compared the diagnostic performance of the DLR_Nomogram to that of the ovarian-adnexal repor...

Assessment of Efficacy and Accuracy of Cervical Cytology Screening With Artificial Intelligence Assistive System.

The role of artificial intelligence (AI) in pathology offers many exciting new possibilities for improving patient care. This study contributes to thi...

The Evolving Landscape of Cervical Cancer: Breakthroughs in Screening and Therapy Through Integrating Biotechnology and Artificial Intelligence.

Cervical cancer (CC) continues to be a major worldwide health concern, profoundly impacting the lives of countless females worldwide. In low- and midd...

Deep learning-based computer-aided diagnosis system for the automatic detection and classification of lateral cervical lymph nodes on original ultrasound images of papillary thyroid carcinoma: a prospective diagnostic study.

PURPOSE: This study aims to develop a deep learning-based computer-aided diagnosis (CAD) system for the automatic detection and classification of late...

Automatic segmentation of lower limb muscles from MR images of post-menopausal women based on deep learning and data augmentation.

Individual muscle segmentation is the process of partitioning medical images into regions representing each muscle. It can be used to isolate spatiall...

Challenges and strategies for wide-scale artificial intelligence (AI) deployment in healthcare practices: A perspective for healthcare organizations.

Healthcare organizations have realized that Artificial intelligence (AI) can provide a competitive edge through personalized patient experiences, impr...

Optimal Implant Sizing Using Machine Learning Is Associated With Increased Range of Motion After Cervical Disk Arthroplasty.

BACKGROUND AND OBJECTIVES: Cervical disk arthroplasty (CDA) offers the advantage of motion preservation in the treatment of focal cervical pathology. ...

Comparing survival of older ovarian cancer patients treated with neoadjuvant chemotherapy versus primary cytoreductive surgery: Reducing bias through machine learning.

OBJECTIVE: To develop and evaluate a multidimensional comorbidity index (MCI) that identifies ovarian cancer patients at risk of early mortality more ...

Predicting central cervical lymph node metastasis in papillary thyroid microcarcinoma using deep learning.

BACKGROUND: The aim of this study is to design a deep learning (DL) model to preoperatively predict the occurrence of central lymph node metastasis (C...

Introducing surgical intelligence in gynecology: Automated identification of key steps in hysterectomy.

OBJECTIVE: The analysis of surgical videos using artificial intelligence holds great promise for the future of surgery by facilitating the development...

Mapping cell-to-tissue graphs across human placenta histology whole slide images using deep learning with HAPPY.

Accurate placenta pathology assessment is essential for managing maternal and newborn health, but the placenta's heterogeneity and temporal variabilit...

Explainable artificial intelligence analysis of brachytherapy boost receipt in cervical cancer during the COVID-19 era.

PURPOSE: Brachytherapy is a critical component of the standard-of-care curative radiotherapy regimen for women with locally advanced cervical cancer (...

Benchmarking machine learning-based real-time respiratory signal predictors in 4D SBRT.

BACKGROUND: Stereotactic body radiotherapy of thoracic and abdominal tumors has to account for respiratory intrafractional tumor motion. Commonly, an ...

Prediction of metabolic syndrome following a first pregnancy.

BACKGROUND: The prevalence of metabolic syndrome is rapidly increasing in the United States. We hypothesized that prediction models using data obtaine...

Multi-center Dose Prediction Using Attention-aware Deep learning Algorithm Based on Transformers for Cervical Cancer Radiotherapy.

AIMS: Accurate dose delivery is crucial for cervical cancer volumetric modulated arc therapy (VMAT). We aimed to develop a robust deep-learning (DL) a...

Prospective validation of a machine learning model for applicator and hybrid interstitial needle selection in high-dose-rate (HDR) cervical brachytherapy.

PURPOSE: To Demonstrate the clinical validation of a machine learning (ML) model for applicator and interstitial needle prediction in gynecologic brac...

A Deep Learning-Based Assessment Pipeline for Intraepithelial and Stromal Tumor-Infiltrating Lymphocytes in High-Grade Serous Ovarian Carcinoma.

Tumor-infiltrating lymphocytes (TILs) are associated with improved survival in patients with epithelial ovarian cancer. However, TIL evaluation has no...

Artificial Intelligence (AI) for Early Diagnosis of Retinal Diseases.

Artificial intelligence (AI) has emerged as a transformative tool in the field of ophthalmology, revolutionizing disease diagnosis and management. Thi...

Improving cervical cancer classification in PAP smear images with enhanced segmentation and deep progressive learning-based techniques.

OBJECTIVE: Cervical cancer, a prevalent and deadly disease among women, comes second only to breast cancer, with over 700 daily deaths. The Pap smear ...