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cloud-based machine-learning platform aiforia cloud and create  (Aiforia Technologies)

 
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    Structured Review

    Aiforia Technologies cloud-based machine-learning platform aiforia cloud and create
    The characteristics of the studies included regarding the application of <t> machine learning </t> to image analysis in clinical microbiology laboratory.
    Cloud Based Machine Learning Platform Aiforia Cloud And Create, supplied by Aiforia Technologies, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/cloud-based+machine+learning+system/cloud+based+platform+aiforia/pmc10930720-23-21-24
    Average 90 stars, based on 1 article reviews
    cloud-based machine-learning platform aiforia cloud and create - by Bioz Stars, 2026-10
    90/100 stars

    Images

    1) Product Images from "Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review"

    Article Title: Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review

    Journal: Diagnostics

    doi: 10.3390/diagnostics14050484

    The characteristics of the studies included regarding the application of  machine learning  to image analysis in clinical microbiology laboratory.
    Figure Legend Snippet: The characteristics of the studies included regarding the application of machine learning to image analysis in clinical microbiology laboratory.

    Techniques Used: Biomarker Discovery, Microscopy, Spectroscopy, Infection, Staining, Plasmid Preparation, Imaging, Agglutination, Confocal Microscopy, Raman Spectroscopy, Virus, Reverse Transcription, Amplification, Lamp Assay, Transmission Assay, Electron Microscopy

    Related Articles

    other:

    Article Title: Manually pressurized droplet digital PCR chip for rapid SARS-CoV-2 diagnostics
    Article Snippet: Chip was imaged, and results were analyzed using a cloud-based image analysis platform, Aiforia, in which deep convolutional neural networks are used to count the droplets.

    Article Title: Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review
    Article Snippet: Holmström et al. [ ] (2020) , 125 , Thin blood and Giemsa-stained thick smear microscopy , Microscopic smear images , Cloud-based machine-learning platform (Aiforia Cloud and Create), GoogLeNet network , Digitalization of blood smears, application of deep learning (DL) algorithms to detect Plasmodium falciparum.

    Article Title: A novel automated morphological analysis of Iba1+ microglia using a deep learning assisted model
    Article Snippet: The cloud-based platform Aiforia ® enabled us to develop a robust microglia detection model that we could generalize across 2 additional datasets, each with its own model. Dataset specific generalization optimizes model performance in each of the adapted datasets ( ).

    Article Title: Neuropathological Correlates of White Matter Hyperintensities in Cerebral Amyloid Angiopathy
    Article Snippet: BACKGROUND: White matter hyperintensities (WMHs) are frequently observed on magnetic resonance imaging (MRI) in patients with cerebral amyloid angiopathy (CAA).. The neuropathological substrates that underlie WMHs in CAA are unclear, and it remains largely unexplored whether the different WMH distribution patterns associated with CAA (posterior confluent and subcortical multispot) reflect alternative pathophysiological mechanisms.

    Article Title: Performance of an Artificial Intelligence Model for Recognition and Quantitation of Histologic Features of Eosinophilic Esophagitis on Biopsy Samples.
    Article Snippet: We have developed an artificial intelligence (AI)ebased digital pathology model for the evaluation of histologic features related to eosinophilic esophagitis (EoE).. In this study, we evaluated the performance of our AI model in a cohort of pediatric and adult patients for histologic features included in the Eosinophilic Esophagitis Histologic Scoring System (EoEHSS).. We collected a total of 203 esophageal biopsy samples from patients with mucosal eosinophilia of any degree (91 adult and 112 pediatric patients) and 10 normal controls from a prospectively maintained database.

    Article Title: Cortical superficial siderosis is associated with reactive astrogliosis in cerebral amyloid angiopathy
    Article Snippet: Digitized sections were uploaded to the cloud-based Aiforia ® platform, where deep-learning algorithms were utilized to detect tissue, iron deposits, and GFAP-positive and CD68-positive cells.

    Article Title: Causal debiasing for unknown bias in histopathology-A colon cancer use case.
    Article Snippet: After staining, all images were uploaded to the Aiforia Create deep learning cloud-based platform (Aiforia Technologies, Helsinki, Finland), a commercially available platform designed explicitly for histologic images.

    Software:

    Article Title: Stroma-mediated breast cancer cell proliferation indirectly drives chemoresistance by accelerating tumor recovery between chemotherapy cycles
    Article Snippet: .. An AIbased software (Aiforia) was trained to recognize the nuclei of proliferating (stained with the proliferation marker BrdU) and nonproliferating tumor cells and record the x-y coordinates of the center of the nuclei. ..

    Staining:

    Article Title: Stroma-mediated breast cancer cell proliferation indirectly drives chemoresistance by accelerating tumor recovery between chemotherapy cycles
    Article Snippet: .. An AIbased software (Aiforia) was trained to recognize the nuclei of proliferating (stained with the proliferation marker BrdU) and nonproliferating tumor cells and record the x-y coordinates of the center of the nuclei. ..

    Marker:

    Article Title: Stroma-mediated breast cancer cell proliferation indirectly drives chemoresistance by accelerating tumor recovery between chemotherapy cycles
    Article Snippet: .. An AIbased software (Aiforia) was trained to recognize the nuclei of proliferating (stained with the proliferation marker BrdU) and nonproliferating tumor cells and record the x-y coordinates of the center of the nuclei. ..



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    Image Search Results


    The characteristics of the studies included regarding the application of  machine learning  to image analysis in clinical microbiology laboratory.

    Journal: Diagnostics

    Article Title: Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review

    doi: 10.3390/diagnostics14050484

    Figure Lengend Snippet: The characteristics of the studies included regarding the application of machine learning to image analysis in clinical microbiology laboratory.

    Article Snippet: Holmström et al. [ ] (2020) , 125 , Thin blood and Giemsa-stained thick smear microscopy , Microscopic smear images , Cloud-based machine-learning platform (Aiforia Cloud and Create), GoogLeNet network , Digitalization of blood smears, application of deep learning (DL) algorithms to detect Plasmodium falciparum.

    Techniques: Biomarker Discovery, Microscopy, Spectroscopy, Infection, Staining, Plasmid Preparation, Imaging, Agglutination, Confocal Microscopy, Raman Spectroscopy, Virus, Reverse Transcription, Amplification, Lamp Assay, Transmission Assay, Electron Microscopy