AIMC Topic: Biopsy

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Intraoperative cytological diagnosis of brain tumours: A preliminary study using a deep learning model.

Cytopathology : official journal of the British Society for Clinical Cytology
BACKGROUND: Intraoperative pathological diagnosis of central nervous system (CNS) tumours is essential to planning patient management in neuro-oncology. Frozen section slides and cytological preparations provide architectural and cellular information...

The Role of a Deep Learning-Based Computer-Aided Diagnosis System and Elastography in Reducing Unnecessary Breast Lesion Biopsies.

Clinical breast cancer
OBJECTIVES: Ultrasound examination has inter-observer and intra-observer variability and a high false-positive rate. The aim of this study was to evaluate the value of the combined use of a deep learning-based computer-aided diagnosis (CAD) system an...

Estimating diagnostic uncertainty in artificial intelligence assisted pathology using conformal prediction.

Nature communications
Unreliable predictions can occur when an artificial intelligence (AI) system is presented with data it has not been exposed to during training. We demonstrate the use of conformal prediction to detect unreliable predictions, using histopathological d...

Improving quality control in the routine practice for histopathological interpretation of gastrointestinal endoscopic biopsies using artificial intelligence.

PloS one
BACKGROUND: Colorectal and gastric cancer are major causes of cancer-related deaths. In Korea, gastrointestinal (GI) endoscopic biopsy specimens account for a high percentage of histopathologic examinations. Lack of a sufficient pathologist workforce...

Novel approaches utilizing robotic navigational bronchoscopy: a single institution experience.

Journal of robotic surgery
The effective biopsy of pulmonary nodules is crucial to early diagnosis and consequent effective treatment for patients. As a relatively new procedure, few studies look at the effectiveness of the Monarch system in achieving this goal. The aim of thi...

Deep learning-based quantification of NAFLD/NASH progression in human liver biopsies.

Scientific reports
Non-alcoholic fatty liver disease (NAFLD) affects about 24% of the world's population. Progression of early stages of NAFLD can lead to the more advanced form non-alcoholic steatohepatitis (NASH), and ultimately to cirrhosis or liver cancer. The curr...

Quantitative Radiological Features and Deep Learning for the Non-Invasive Evaluation of Programmed Death Ligand 1 Expression Levels in Gastric Cancer Patients: A Digital Biopsy Study.

Academic radiology
RATIONALE AND OBJECTIVES: Programmed Death-Ligand 1 (PD-L1) is an important biomarker for patient selection of immunotherapy in gastric cancer (GC). This study aimed to construct and validate a non-invasive virtual biopsy system based on radiological...

A deep learning network for Gleason grading of prostate biopsies using EfficientNet.

Biomedizinische Technik. Biomedical engineering
OBJECTIVES: The most crucial part in the diagnosis of cancer is severity grading. Gleason's score is a widely used grading system for prostate cancer. Manual examination of the microscopic images and grading them is tiresome and consumes a lot of tim...

Clinical safety and efficacy of a fully automated robot for magnetic resonance imaging-guided breast biopsy.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: Magnetic resonance imaging (MRI)-guided biopsies are an accurate, but technically challenging, method for screening and diagnosis of breast lesions. This study assesses the safety and efficacy of an Image Guided Automated Robot (IGAR) in ...

Virtual Biopsy by Using Artificial Intelligence-based Multimodal Modeling of Binational Mammography Data.

Radiology
Background Computational models based on artificial intelligence (AI) are increasingly used to diagnose malignant breast lesions. However, assessment from radiologic images of the specific pathologic lesion subtypes, as detailed in the results of bio...