AIMC Topic: Endoscopy

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Robust multi-label surgical tool classification in noisy endoscopic videos.

Scientific reports Feb 14, 2025
Over the past few years, surgical data science has attracted substantial interest from the machine learning (ML) community. Various studies have demonstrated the efficacy of emerging ML techniques in analysing surgical data, particularly recordings o...
Algorithms Endoscopy Video Recording Humans Machine Learning
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Ultrasound Predicts Drug-Induced Sleep Endoscopy Findings Using Machine Learning Models.

The Laryngoscope Jan 29, 2025
OBJECTIVES: Ultrasound is a promising low-risk imaging modality that can provide objective airway measurements that may circumvent limitations of drug-induced sleep endoscopy (DISE). This study was devised to identify ultrasound-derived anatomical me...
Sleep Predictive Value of Tests Sleep Apnea, Obstructive Endoscopy Male Female Middle Aged Aged Humans Ultrasonography Adult Machine Learning Hypnotics and Sedatives
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Fluorescence Lifetime Endoscopy with a Nanosecond Time-Gated CAPS Camera with IRF-Free Deep Learning Method.

Sensors (Basel, Switzerland) Jan 14, 2025
Fluorescence imaging has been widely used in fields like (pre)clinical imaging and other domains. With advancements in imaging technology and new fluorescent labels, fluorescence lifetime imaging is gradually gaining recognition. Our research departm...
Humans Endoscopy Optical Imaging Phantoms, Imaging Fluorescence Deep Learning Image Processing, Computer-Assisted
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Deep learning multi-classification of middle ear diseases using synthetic tympanic images.

Acta oto-laryngologica Jan 10, 2025
BACKGROUND: Recent advances in artificial intelligence have facilitated the automatic diagnosis of middle ear diseases using endoscopic tympanic membrane imaging.
Humans Ear Diseases Otitis Media Tympanic Membrane Ear, Middle Endoscopy Deep Learning
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Machine Learning of Endoscopy Images to Identify, Classify, and Segment Sinonasal Masses.

International forum of allergy & rhinology Jan 8, 2025
BACKGROUND: We developed and assessed the performance of a machine learning model (MLM) to identify, classify, and segment sinonasal masses based on endoscopic appearance.
Nasal Polyps Paranasal Sinuses Nose Neoplasms Humans Paranasal Sinus Neoplasms Male Neural Networks, Computer Middle Aged Machine Learning Female Endoscopy
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Development and Validation of an Explainable Prediction Model for Postoperative Recurrence in Pediatric Chronic Rhinosinusitis.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery Dec 17, 2024
OBJECTIVE: This study aims to develop an interpretable machine learning (ML) predictive model to assess its efficacy in predicting postoperative recurrence in pediatric chronic rhinosinusitis (CRS).
Rhinitis Retrospective Studies Chronic Disease Female Rhinosinusitis Male Humans Child, Preschool Postoperative Period Endoscopy Machine Learning Child Recurrence Sinusitis Adolescent
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Single-Port Three-Dimensional Endoscopic-Assisted Axillary Lymph Node Dissection (S-P 3D E-ALND): Surgical Technique and Preliminary Results.

The breast journal Dec 4, 2024
Endoscopic-assisted breast surgery (EABS) provides better cosmetic outcomes for breast cancer patients with small incisions in an inconspicuous area. However, an extended incision and heavy assistant retraction are usually required for an adequate e...
Operative Time Endoscopy Breast Neoplasms Female Humans Middle Aged Aged Retrospective Studies Lymph Node Excision Adult Axilla
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Automated Detection of Gastrointestinal Diseases Using Resnet50*-Based Explainable Deep Feature Engineering Model with Endoscopy Images.

Sensors (Basel, Switzerland) Dec 2, 2024
This work aims to develop a novel convolutional neural network (CNN) named ResNet50* to detect various gastrointestinal diseases using a new ResNet50*-based deep feature engineering model with endoscopy images. The novelty of this work is the develop...
Humans Endoscopy Neural Networks, Computer Image Processing, Computer-Assisted Algorithms Gastrointestinal Diseases
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SSL-CPCD: Self-Supervised Learning With Composite Pretext-Class Discrimination for Improved Generalisability in Endoscopic Image Analysis.

IEEE transactions on medical imaging Dec 2, 2024
Data-driven methods have shown tremendous progress in medical image analysis. In this context, deep learning-based supervised methods are widely popular. However, they require a large amount of training data and face issues in generalisability to uns...
Humans Algorithms Endoscopy Deep Learning Supervised Machine Learning Image Interpretation, Computer-Assisted
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Real-time augmentation of diagnostic nasal endoscopy video using AI-enabled edge computing.

International forum of allergy & rhinology Sep 24, 2024
AI-enabled augmentation of nasal endoscopy video images is feasible in the clinical setting. Edge computing hardware can interface with existing nasal endoscopy equipment. Real-time AI performance can achieve an acceptable balance of accuracy and eff...
Artificial Intelligence Endoscopy Nose Video Recording Humans
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