AIMC Topic: Humans

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Novel deep learning approach to estimate rigid gas permeable contact lens base curve for keratoconus fitting.

Contact lens & anterior eye : the journal of the British Contact Lens Association
INTRODUCTION: Rigid gas permeable contact lenses (RGP) are the most efficient means of providing optimal vision in keratoconus. RGP fitting can be challenging and time-consuming for ophthalmologists and patients. Deep learning predictive models could...

A Combined Model Integrating Radiomics and Deep Learning Based on Contrast-Enhanced CT for Preoperative Staging of Laryngeal Carcinoma.

Academic radiology
RATIONALE AND OBJECTIVES: Accurate staging of laryngeal carcinoma can inform appropriate treatment decision-making. We developed a radiomics model, a deep learning (DL) model, and a combined model (incorporating radiomics features and DL features) ba...

A deep learning image analysis method for renal perfusion estimation in pseudo-continuous arterial spin labelling MRI.

Magnetic resonance imaging
Accurate segmentation of renal tissues is an essential step for renal perfusion estimation and postoperative assessment of the allograft. Images are usually manually labeled, which is tedious and prone to human error. We present an image analysis met...

Robotic-assisted total hip arthroplasty utilizing a fluoroscopy-guided system resulted in improved intra-operative efficiency relative to a computerized tomography-based platform.

Journal of robotic surgery
Robotic-assisted total hip arthroplasty (THA) using a computerized-tomography (CT) based workflow increases surgical time relative to traditional manual technique. The purpose of this investigation was to compare the intra-operative efficiencies of t...

Accuracy of liver metastasis detection and characterization: Dual-energy CT versus single-energy CT with deep learning reconstruction.

European journal of radiology
PURPOSE: To assess whether image quality differences between SECT (single-energy CT) and DECT (dual-energy CT 70 keV) with equivalent radiation doses result in altered detection and characterization accuracy of liver metastases when using deep learni...

Incorporating entity-level knowledge in pretrained language model for biomedical dense retrieval.

Computers in biology and medicine
In recent years, pre-trained language models (PLMs) have dominated natural language processing (NLP) and achieved outstanding performance in various NLP tasks, including dense retrieval based on PLMs. However, in the biomedical domain, the effectiven...

Recent advancements in machine learning and deep learning-based breast cancer detection using mammograms.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
OBJECTIVE: Mammogram-based automatic breast cancer detection has a primary role in accurate cancer diagnosis and treatment planning to save valuable lives. Mammography is one basic yet efficient test for screening breast cancer. Very few comprehensiv...

Automatic orbital segmentation using deep learning-based 2D U-net and accuracy evaluation: A retrospective study.

Journal of cranio-maxillo-facial surgery : official publication of the European Association for Cranio-Maxillo-Facial Surgery
The purpose of this study was to verify whether the accuracy of automatic segmentation (AS) of computed tomography (CT) images of fractured orbits using deep learning (DL) is sufficient for clinical application. In the surgery of orbital fractures, m...

Real-Time Sensor-Embedded Neural Network for Human Activity Recognition.

Sensors (Basel, Switzerland)
This article introduces a novel approach to human activity recognition (HAR) by presenting a sensor that utilizes a real-time embedded neural network. The sensor incorporates a low-cost microcontroller and an inertial measurement unit (IMU), which is...