Refractive Surgery

Electrocardiogram analysis of post-stroke elderly people using one-dimensional convolutional neural network model with gradient-weighted class activation mapping.

Stroke is the second leading cause of death globally after ischemic heart disease, also a risk factor of cardioembolic stroke. Thus, we postulate that heartbeats encapsulate vital signals related to stroke. With the rapid advancement of deep neural networks (DNNs), it emerges as a powerful tool to decipher intriguing heartbeat patterns associated with post-stroke patients. In this study, we propos...

The adoption of socially assistive robots for long-term care: During COVID-19 and in a post-pandemic society.

The rapid spread of COVID-19 has prompted a surge in the adoption of technology, highlighting a number of potential applications for Socially Assistive Robots (SARs). Our entire healthcare system has been under unprecedented strain, and going forward, we must consider how robotic technology could help improve the quality of care and day-to-day functionality of our care facilities. Herein, we prese...

Accuracy and efficiency of automatic tooth segmentation in digital dental models using deep learning.

This study evaluates the accuracy and efficiency of automatic tooth segmentation in digital dental models using deep learning. We developed a dynamic ...

Robot-Assisted Laparoscopic Calyceo-Pyelostomy for Vascular Compression of the Upper Calyx (Fraley Syndrome).

Fraley's Syndrome is a rare anatomic vascular malformation described in 1966 where an aberrant crossing vessel compresses the upper infundibulum and l...

A review on the application of deep learning for CT reconstruction, bone segmentation and surgical planning in oral and maxillofacial surgery.

Computer-assisted surgery (CAS) allows clinicians to personalize treatments and surgical interventions and has therefore become an increasingly popula...

Patient Benefit and Quality of Life after Robot-Assisted Head and Neck Surgery.

Robotic systems for head and neck surgery are at different stages of technical development and clinical application. Currently, robotic systems are pr...

Automatic Segmentation and Measurement of Choroid Layer in High Myopia for OCT Imaging Using Deep Learning.

Automatic segmentation and measurement of the choroid layer is useful in studying of related fundus diseases, such as diabetic retinopathy and high my...

A machine learning approach applied to gynecological ultrasound to predict progression-free survival in ovarian cancer patients.

In a growing number of social and clinical scenarios, machine learning (ML) is emerging as a promising tool for implementing complex multi-parametric ...

Validation of Soft Labels in Developing Deep Learning Algorithms for Detecting Lesions of Myopic Maculopathy From Optical Coherence Tomographic Images.

PURPOSE: It is common for physicians to be uncertain when examining some images. Models trained with human uncertainty could be a help for physicians ...

Deep Learning for Ocular Disease Recognition: An Inner-Class Balance.

It can be challenging for doctors to identify eye disorders early enough using fundus pictures. Diagnosing ocular illnesses by hand is time-consuming,...

Using Robot-Based Variables during Upper Limb Robot-Assisted Training in Subacute Stroke Patients to Quantify Treatment Dose.

In post-stroke motor rehabilitation, treatment dose description is estimated approximately. The aim of this retrospective study was to quantify the tr...

Deep learning approach for bubble segmentation from hysteroscopic images.

Gas embolism is a potentially serious complication of hysteroscopic surgery. It is particularly necessary to monitor bubble parameters in hysteroscopi...

Explaining spatial accessibility to high-quality nursing home care in the US using machine learning.

In this study we measure and map the system-wide spatial accessibility to good quality nursing home care for all counties in the contiguous United Sta...

The robotic learning curve for a newly appointed colorectal surgeon.

Robotic colorectal surgery allows for better ergonomics, superior retraction, and fine movements in the narrow anatomy of the pelvis. Recent years hav...

Deep learning-based image reconstruction and post-processing methods in positron emission tomography for low-dose imaging and resolution enhancement.

Image processing plays a crucial role in maximising diagnostic quality of positron emission tomography (PET) images. Recently, deep learning methods d...

Collection of 2429 constrained headshots of 277 volunteers for deep learning.

Deep learning has rapidly been filtrating many aspects of human lives. In particular, image recognition by convolutional neural networks has inspired ...

Machine learning methods to predict presence of residual cancer following hysterectomy.

Surgical management for gynecologic malignancies often involves hysterectomy, often constituting the most common gynecologic surgery worldwide. Despit...

Comparative Study of Postoperative Pain Relief Using Preincisional Versus Postincisional Infiltration With 0.25% Bupivacaine in Abdominal Hysterectomy Under General Anesthesia.

BACKGROUND AND AIMS: Hysterectomy, probably the most common non-pregnancy related surgery performed in gynaecology, is associated with moderate to sev...

Deep learning of quantitative ultrasound multi-parametric images at pre-treatment to predict breast cancer response to chemotherapy.

In this study, a novel deep learning-based methodology was investigated to predict breast cancer response to neo-adjuvant chemotherapy (NAC) using the...

Deep Learning Post-Filtering Using Multi-Head Attention and Multiresolution Feature Fusion for Image and Intra-Video Quality Enhancement.

The paper proposes a novel post-filtering method based on convolutional neural networks (CNNs) for quality enhancement of RGB/grayscale images and vid...