ArticleNature biomedical engineering2018
Design and clinical validation of a point-of-care device for the diagnosis of lymphoma via contrast-enhanced microholography and machine learning.
Article in Nature biomedical engineering, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
35 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence performance in detecting lymphoma from medical imaging: a systematic review and meta-analysis.BMC medical informatics and decision making · 2024Pooled it
- Machine learning-driven cancer diagnostics with improved robustness and interpretability.Chemical science · 2026Review
- Therapeutic Drug Monitoring and Point-of-Care Technologies: Opportunities and Current Challenges.Therapeutic drug monitoring · 2026Review
- Integrated Multi-Channel Automatic Cellular Profiling (iMAP) System for Cholangiocarcinoma Diagnosis.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- MIDAS: rapid, multiplexed molecular profiling for integrated host-pathogen analysis.Nature communications · 2025Article
- Computational modelling of aggressive B-cell lymphoma.Biochemical Society transactions · 2025Review
- An energy-embodied paralleled liquid manipulation for equipment-free, quantitative multiplexed liver function monitoring.Science advances · 2025Article
- Space-Confined Amplification for In Situ Imaging of Single Nucleic Acid and Single Pathogen on Biological Samples.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Liquid Biopsy Instrument for Ultra-Fast and Label-Free Detection of Circulating Tumor Cells.Research (Washington, D.C.) · 2024Article
- A microfluidic hemostatic diagnostics platform: Harnessing coagulation-induced adaptive-bubble behavioral perception.Cell reports. Medicine · 2023Article
- Leveraging physiology and artificial intelligence to deliver advancements in health care.Physiological reviews · 2023Review
- Advances in artificial intelligence (AI)-based diagnosis in clinical practice-correspondence.Annals of medicine and surgery (2012) · 2023Article
- Smartphone-based platforms implementing microfluidic detection with image-based artificial intelligence.Nature communications · 2023Review
- The Impact of Artificial Intelligence on Health Equity in Oncology: Scoping Review.Journal of medical Internet research · 2022Article
- Evaluation of an Integrated Spectroscopy and Classification Platform for Point-of-Care Core Needle Biopsy Assessment: Performance Characteristics from Ex Vivo Renal Mass Biopsies.Journal of vascular and interventional radiology : JVIR · 2022Article
- Space-time-regulated imaging analyzer for smart coagulation diagnosis.Cell reports. Medicine · 2022Article
- Multiplexed imaging in oncology.Nature biomedical engineering · 2022Review
- Utilization of Artificial Intelligence in Disease Prevention: Diagnosis, Treatment, and Implications for the Healthcare Workforce.Healthcare (Basel, Switzerland) · 2022Review
- Machine Learning Based Lens-Free Shadow Imaging Technique for Field-Portable Cytometry.Biosensors · 2022Article
- Computational Optics for Point-of-Care Breast Cancer Profiling.Methods in molecular biology (Clifton, N.J.) · 2022Article
Corrections and comments
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Authors and funding
18 authors.
Funding
Abstract
The identification of patients with aggressive cancer who require immediate therapy is a health challenge in low-income and middle-income countries. Limited pathology resources, high healthcare costs and large-case loads call for the development of advanced standalone diagnostics. Here, we report and validate an automated, low-cost point-of-care device for the molecular diagnosis of aggressive lymphomas. The device uses contrast-enhanced microholography and a deep-learning algorithm to directly analyse percutaneously obtained fine-needle aspirates. We show the feasibility and high accuracy of the device in cells, as well as the prospective validation of the results in 40 patients clinically referred for image-guided aspiration of nodal mass lesions suspicious for lymphoma. Automated analysis of human samples with the portable device should allow for the accurate classification of patients with benign and malignant adenopathy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.