Computer-assisted Diagnosis
Computer-assisted Diagnosis - Related Videos
09:20
Microwave Assisted Rapid Diagnosis of Plant Virus Diseases by Transmission Electron Microscopy
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Cited by 16 •
2011
This study describes a method that allows the rapid and clear diagnosis of plant virus diseases in about half a day by using a combination of microwave assisted plant sample preparation for transmission electron microscopy and negative staining methods.
07:34
FISH for Pre-implantation Genetic Diagnosis
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Cited by 17 •
2011
This article describes the selection of suitable probes for single-cell FISH, spreading techniques for blastomere nuclei, and in situ hybridization and signal scoring, applied to pre-implantation genetic diagnosis (PGD) in a clinical setting.
11:07
Robotic-assisted Lateral Pancreaticojejunostomy for Chronic Pancreatitis
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2025
This protocol presents a method for performing a parenchymal sparing robotic-assisted lateral pancreaticojejunostomy intended for appropriately selected patients with a pancreatic duct obstruction without significant inflammation of the pancreatic head. This article demonstrates critical aspects of the operation in a minimally invasive, robotic fashion to treat recurrent acute/chronic pancreatitis.
06:55
Diagnosis of Neoplasia in Barrett’s Esophagus using Vital-dye Enhanced Fluorescence Imaging
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Cited by 1 •
2014
Vital-dye enhanced fluorescence imaging (VFI) is a novel in vivo technique that combines high-resolution epithelial imaging with exogenous topical fluorescent contrast to highlight glandular morphology and delineate neoplasia (high grade dysplasia and cancer) in the distal esophagus.
01:22
Nursing Diagnosis
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2023
Following assessment, a nursing diagnosis is the next step in the nursing process. It begins after the nurse has collected and recorded the patient data. The purpose of diagnosing is to identify how the client responds to actual or potential health processes, identify factors that bestow or that cause health problems, the etiologies, and identify resources or strengths the individual, group, or community can draw on to prevent or resolve problems. The nursing diagnosis focuses on evidence-based...
FAQs
The system analyzes sources such as neuroimaging, electrophysiological recordings, and other clinical measurements by identifying measurable features within the data. It then compares those features with reference patterns and applies statistical or machine-learning models to estimate diagnostic likelihood. This sequence helps organize complex information for review without converting an automated estimate into a definitive clinical judgment.
Feature extraction converts clinical measurements into characteristics that a computational model can evaluate, while reference patterns provide a basis for comparison. Together, they allow software to identify similarities or differences associated with abnormalities or neurological disorders. The quality and representativeness of those patterns directly affect how meaningfully the resulting estimates can support interpretation.
Representative training data help models reflect the range of cases they may encounter, rather than learning patterns that apply only to a narrow group. Rigorous validation tests whether performance remains dependable beyond the data used to develop the system. These safeguards are essential because unreliable training or evaluation can increase errors and weaken clinical usefulness.
Computational estimates should function as decision support, not as replacements for clinicians. A professional can examine the original measurements, consider patient-specific context, and judge whether the software’s suggested pattern is plausible. This relationship is especially important when data are incomplete, findings are ambiguous, or the system’s estimate conflicts with expert interpretation.
A typical workflow begins with collecting relevant neuroimaging, electrophysiological, or other clinical measurements. Software processes the data, extracts features, compares them with reference patterns, and generates a statistical or machine-learning estimate. Clinicians then review the output alongside the underlying evidence. The workflow can also help prioritize cases that require further professional assessment.
In neuroscience, these systems can assist with detecting and classifying neurological disorders, identifying abnormalities, and recognizing patterns associated with diagnostic risk. They may also standardize parts of interpretation and help prioritize cases for review. Their practical value is reflected in more reproducible assessment and potentially more timely attention, provided validation and clinical oversight remain central.