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Overcome the challenges of subjective and time-consuming manual PD-L1 scoring in lung cancer – Aiforia® Lung Cancer Suite can reduce errors and help standardize scoring by providing AI-driven PD-L1 assessments that are objective and reproducible.
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Automatically detects invasive carcinoma and delivers standardized, reproducible tumor proportion scoring (TPS), enhancing diagnostic accuracy and efficiency. It assists pathologists in overcoming the challenges of subjective and time-consuming manual PD-L1 scoring in lung cancer.
CE-IVD marked for diagnostic use in EU and EEA countries, and for Research Use Only (RUO) and Performance Studies Only (PSO) in all other market areas
Automatically detects invasive carcinoma and delivers standardized, reproducible tumor proportion scoring (TPS), enhancing diagnostic accuracy and efficiency. It assists pathologists in overcoming the challenges of subjective and time-consuming manual PD-L1 scoring in lung cancer.
CE-IVD marked for diagnostic use in EU and EEA countries, and for Research Use Only (RUO) and Performance Studies Only (PSO) in all other market areas
Aiforia takes accuracy beyond standard AI heatmaps by clearly marking the exact tumor areas with detailed visual overlays directly on the digital slides, enabling easy visual confirmation at any magnification level. This approach empowers pathologists with both visual transparency and exact quantitative data, such as accurate tumor area measurements and cell counts, delivering actionable insights.
Aiforia® Clinical Suites conveniently combine results from multiple AI models, WSIs, and stainings into a case-level view and report for first-read diagnostic support.
The AI results are populated directly into the reporting panel. The user can utilize advanced features like case list triaging, pre-ordering of extra stains, and flagging of non-representative samples for a faster workflow.
Aiforia® Lung Cancer PD-L1 provides standardized scoring, with high accuracy, increasing the reliability of PD-L1 analysis.
This webinar takes you inside the real-world diagnostic pressure points of PD-L1 TPS testing in NSCLC.
University Hospital Southampton is implementing Aiforia’s clinical PD-L1 lung cancer AI solution to assess PD-L1 staining in lung cancer specimens.
Dr. Jennifer M. Boland et al. from the Mayo Clinic successfully developed an AI model to determine the invasion in pulmonary adenocarcinoma.
Dr. Maxwell L. Smith from Mayo Clinic developed two AI models to detect and quantify histopathologic features in interstitial lung disease (ILD) with the goal of using AI to help differentiate ILD subtypes.
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Liesbeth Hondelink, MSc student at Leiden University Medical Centre, describes using AI to automate the quantification of PD-L1 in lung cancer studies.
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Labs in the Netherlands are joining together to launch an AI model validation study for PD-L1 scoring with a goal of multi-source domain adaptation.
