Obstetrics & Gynecology

Deep learning based analysis of dynamic video ultrasonography for predicting cervical lymph node metastasis in papillary thyroid carcinoma.

BACKGROUND: Cervical lymph node metastasis (CLNM) is the most common form of thyroid cancer metastasis. Accurate preoperative CLNM diagnosis is of more importance in patients with papillary thyroid cancer (PTC). However, there is currently no unified methods to objectively predict CLNM risk from ultrasonography in PTC patients.This study aimed to develop a deep learning (DL) model to help clinicia...

Magnetic soft microrobots for erectile dysfunction therapy.

Erectile dysfunction (ED) is a major threat to male fertility and quality of life, and mesenchymal stromal cells (MSCs) are a promising therapeutic option. However, therapeutic outcomes are compromised by low MSC retention and survival rates in corpus cavernosum tissue. Here, we developed an innovative magnetic soft microrobot comprising an ultrasoft hydrogel microsphere embedded with a magnetic n...

An improved cancer diagnosis algorithm for protein mass spectrometry based on PCA and a one-dimensional neural network combining ResNet and SENet.

Cancer is one of the most serious health problems worldwide. Because cancer has no specific symptoms in its early stages, it is often not diagnosed un...

Attention 3D UNET for dose distribution prediction of high-dose-rate brachytherapy of cervical cancer: Intracavitary applicators.

BACKGROUND: Formulating a clinically acceptable plan within the time-constrained clinical setting of brachytherapy poses challenges to clinicians. Dee...

Artificial intelligence-created personal statements compared with applicant-written personal statements: a survey of obstetric anesthesia fellowship program directors in the United States.

BACKGROUND: A personal statement is a common requirement in medical residency and fellowship applications. Generative artificial intelligence may be u...

A deep learning model for predicting blastocyst formation from cleavage-stage human embryos using time-lapse images.

Efficient prediction of blastocyst formation from early-stage human embryos is imperative for improving the success rates of assisted reproductive tec...

Water-Stable Magnetic Lipiodol Micro-Droplets as a Miniaturized Robotic Tool for Drug Delivery.

Magnetic microrobots, designed to navigate the complex environments of the human body, show promise for minimally invasive diagnosis and treatment. Ho...

Elevating Patient Care With Deep Learning: High-Resolution Cervical Auscultation Signals for Swallowing Kinematic Analysis in Nasogastric Tube Patients.

Patients with nasogastric (NG) tubes require careful monitoring due to the potential impact of the tube on their ability to swallow safely. This study...

A F-FDG PET/CT-based deep learning-radiomics-clinical model for prediction of cervical lymph node metastasis in esophageal squamous cell carcinoma.

BACKGROUND: To develop an artificial intelligence (AI)-based model using Radiomics, deep learning (DL) features extracted from F-fluorodeoxyglucose (F...

Prediction of clinical pregnancy outcome after single fresh blastocyst transfer during in vitro fertilization: an ensemble learning perspective.

To establish a predictive model for clinical pregnancy outcomes following the transfer of a single fresh blastocyst in vitro fertilization (IVF). 615 ...

Miniature Robots for Battling Bacterial Infection.

Micro/nanorobots have shown great promise for minimally invasive bacterial infection therapy. However, bacterial infections usually form biofilms insi...

Leveraging machine learning to streamline the development of liposomal drug delivery systems.

Drug delivery systems efficiently and safely administer therapeutic agents to specific body sites. Liposomes, spherical vesicles made of phospholipid ...

Healthy nutrition and weight management for a positive pregnancy experience in the antenatal period: Comparison of responses from artificial intelligence models on nutrition during pregnancy.

BACKGROUND: As artificial intelligence AI-supported applications become integral to web-based information-seeking, assessing their impact on healthy n...

Automatic delineation of cervical cancer target volumes in small samples based on multi-decoder and semi-supervised learning and clinical application.

Radiotherapy has been demonstrated to be one of the most significant treatments for cervical cancer, during which accurate and efficient delineation o...

Edge computing-based ensemble learning model for health care decision systems.

A growing number of humans have suffered severe chronic illnesses, which has caused a boost in the requirement for diagnostic and medical treatment pr...

Machine learning models in evaluating the malignancy risk of ovarian tumors: a comparative study.

OBJECTIVES: The study aimed to compare the diagnostic efficacy of the machine learning models with expert subjective assessment (SA) in assessing the ...

Revolutionizing spinal interventions: a systematic review of artificial intelligence technology applications in contemporary surgery.

Leveraging its ability to handle large and complex datasets, artificial intelligence can uncover subtle patterns and correlations that human observati...

Machine learning models for predicting dysphonia following anterior cervical discectomy and fusion: a Swedish Registry Study.

BACKGROUND: Dysphonia is one of the more common complications following anterior cervical discectomy and fusion (ACDF). ACDF is the gold standard for ...

Utilizing machine learning and molecular dynamics for enhanced drug delivery in nanoparticle systems.

Materials data science and machine learning (ML) are pivotal in advancing cancer treatment strategies beyond traditional methods like chemotherapy. Na...

An energy-aware protocol in wireless sensor networks using the scattered search algorithm and fuzzy logic.

Given the resource limitations of wireless sensor networks (WSNs), energy conservation is of utmost importance. Moreover, minimizing data collection d...