AIMC Topic: Pathology, Molecular

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A Perspective on Artificial Intelligence for Molecular Pathologists.

The Journal of molecular diagnostics : JMD Feb 13, 2025
The widespread adoption of next-generation sequencing technology in molecular pathology has enabled us to interrogate the genome as never before. The huge quantities of data generated by sequencing, the enormous complexity of human and microbial gene...
Humans Pathologists High-Throughput Nucleotide Sequencing Pathology, Molecular Artificial Intelligence Machine Learning Computational Biology
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[Explainable artificial intelligence in pathology].

Pathologie (Heidelberg, Germany) Feb 5, 2024
With the advancements in precision medicine, the demands on pathological diagnostics have increased, requiring standardized, quantitative, and integrated assessments of histomorphological and molecular pathological data. Great hopes are placed in art...
Artificial Intelligence Hope Precision Medicine Pathology, Molecular
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Deep Learning Assisted Surface-Enhanced Raman Spectroscopy (SERS) for Rapid and Direct Nucleic Acid Amplification and Detection: Toward Enhanced Molecular Diagnostics.

ACS nano Sep 13, 2023
Surface-enhanced Raman scattering (SERS) has evolved into a robust analytical technique capable of detecting a variety of biomolecules despite challenges in securing a reliable Raman signal. Conventional SERS-based nucleic acid detection relies on hy...
Deep Learning Humans Spectrum Analysis, Raman COVID-19 Reproducibility of Results Zinc Oxide COVID-19 Testing Nucleic Acids Pathology, Molecular
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[Artificial intelligence: a solution for the lack of pathologists?].

Der Pathologe Apr 11, 2022
Given the rapid developments, there is no doubt that artificial intelligence (AI) will substantially impact pathological diagnostics. However, it remains an open question if AI will primarily be another diagnostic tool, such as immunohistochemistry, ...
Artificial Intelligence Forecasting Pathologists Humans Immunohistochemistry Pathology, Molecular
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Accurate diagnosis and prognosis prediction of gastric cancer using deep learning on digital pathological images: A retrospective multicentre study.

EBioMedicine Oct 19, 2021
BACKGROUND: To reduce the high incidence and mortality of gastric cancer (GC), we aimed to develop deep learning-based models to assist in predicting the diagnosis and overall survival (OS) of GC patients using pathological images.
Area Under Curve Neoplasm Staging Neoplasm Grading ROC Curve Image Processing, Computer-Assisted Immunohistochemistry Pathology, Molecular Retrospective Studies Female Stomach Neoplasms Biomarkers, Tumor Male Deep Learning Humans Algorithms
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Explaining multivariate molecular diagnostic tests via Shapley values.

BMC medical informatics and decision making Jul 8, 2021
BACKGROUND: Machine learning (ML) can be an effective tool to extract information from attribute-rich molecular datasets for the generation of molecular diagnostic tests. However, the way in which the resulting scores or classifications are produced ...
Pathology, Molecular Algorithms Cohort Studies Humans Machine Learning
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Deep learning in histopathology: the path to the clinic.

Nature medicine May 14, 2021
Machine learning techniques have great potential to improve medical diagnostics, offering ways to improve accuracy, reproducibility and speed, and to ease workloads for clinicians. In the field of histopathology, deep learning algorithms have been de...
Algorithms Precision Medicine Deep Learning Pathology, Molecular Humans
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Digital/Computational Technology for Molecular Cytology Testing: A Short Technical Note with Literature Review.

Acta cytologica Apr 30, 2021
This short article describes the method of digital cytopathology using Z-stack scanning with or without extended focusing. This technology is suitable to observe such thick clusters as adenocarcinoma on cytologic specimens. Artificial intelligence (A...
Diagnosis, Computer-Assisted Artificial Intelligence Humans Molecular Diagnostic Techniques Automation, Laboratory Image Processing, Computer-Assisted DNA Mutational Analysis Adenocarcinoma of Lung Predictive Value of Tests Reproducibility of Results Lung Neoplasms ErbB Receptors Pathology, Molecular Mutation
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Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics.

EMBO molecular medicine Feb 12, 2020
Limited therapy options due to antibiotic resistance underscore the need for optimization of current diagnostics. In some bacterial species, antimicrobial resistance can be unambiguously predicted based on their genome sequence. In this study, we seq...
Machine Learning Microbial Sensitivity Tests Pseudomonas aeruginosa Pathology, Molecular Genome, Bacterial Anti-Bacterial Agents Drug Resistance, Bacterial Transcriptome
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DeePathology: Deep Multi-Task Learning for Inferring Molecular Pathology from Cancer Transcriptome.

Scientific reports Nov 11, 2019
Despite great advances, molecular cancer pathology is often limited to the use of a small number of biomarkers rather than the whole transcriptome, partly due to computational challenges. Here, we introduce a novel architecture of Deep Neural Network...
Organ Specificity Gene Expression Profiling Pathology, Molecular Data Mining Algorithms Transcriptome Humans Deep Learning Disease Susceptibility Computational Biology Reproducibility of Results Neural Networks, Computer Gene Expression Regulation, Neoplastic Neoplasms
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