AI Medical Compendium Topic

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Carcinoma, Squamous Cell

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Ability of artificial intelligence to detect T1 esophageal squamous cell carcinoma from endoscopic videos and the effects of real-time assistance.

Scientific reports
Diagnosis using artificial intelligence (AI) with deep learning could be useful in endoscopic examinations. We investigated the ability of AI to detect superficial esophageal squamous cell carcinoma (ESCC) from esophagogastroduodenoscopy (EGD) videos...

Tongue Tumor Detection in Hyperspectral Images Using Deep Learning Semantic Segmentation.

IEEE transactions on bio-medical engineering
OBJECTIVE: The utilization of hyperspectral imaging (HSI) in real-time tumor segmentation during a surgery have recently received much attention, but it remains a very challenging task.

Propensity matched analysis of short term oncological and perioperative outcomes following robotic and thoracolaparoscopic esophagectomy for carcinoma esophagus- the first Indian experience.

Journal of robotic surgery
Thoracolaparoscopic esophagectomy (TLE) for carcinoma esophagus has better short-term outcomes compared to open esophagectomy. The precise role of robot-assisted laparoscopic esophagectomy (RALE) is still evolving. Single center retrospective analysi...

An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning.

Nature communications
Deep learning for digital pathology is hindered by the extremely high spatial resolution of whole-slide images (WSIs). Most studies have employed patch-based methods, which often require detailed annotation of image patches. This typically involves l...

Esophageal squamous dysplasia and cancer: Is artificial intelligence our best weapon?

Best practice & research. Clinical gastroenterology
Esophageal cancer is the eight most common cancer in the world and is associated with a poor prognosis. Significant efforts are necessary to improve the detection of early squamous cell cancer such that curative endoscopic therapy can be offered. Stu...

Development and validation of an artificial intelligence system for grading colposcopic impressions and guiding biopsies.

BMC medicine
BACKGROUND: Colposcopy diagnosis and directed biopsy are the key components in cervical cancer screening programs. However, their performance is limited by the requirement for experienced colposcopists. This study aimed to develop and validate a Colp...

Interpretable deep learning systems for multi-class segmentation and classification of non-melanoma skin cancer.

Medical image analysis
We apply for the first-time interpretable deep learning methods simultaneously to the most common skin cancers (basal cell carcinoma, squamous cell carcinoma and intraepidermal carcinoma) in a histological setting. As these three cancer types constit...

Machine learning-based FDG PET-CT radiomics for outcome prediction in larynx and hypopharynx squamous cell carcinoma.

Clinical radiology
AIM: To determine whether machine learning-based radiomic feature analysis of baseline integrated 2-[F]-fluoro-2-deoxy-d-glucose (FDG) positron-emission tomography (PET) computed tomography (CT) predicts disease progression in patients with locally a...

Detecting mouse squamous cell carcinoma from submicron full-field optical coherence tomography images by deep learning.

Journal of biophotonics
The standard medical practice for cancer diagnosis requires histopathology, which is an invasive and time-consuming procedure. Optical coherence tomography (OCT) is an alternative that is relatively fast, noninvasive, and able to capture three-dimens...