ArticleBioengineering (Basel, Switzerland)2026
Evaluating the Performance of LYDIA: An AI-Powered Assistant in the Detection of Metastatic Tumors to Optimize Clinical Workflows and Inform Soft Tissue Surgical Decision-Making.
Article in Bioengineering (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The integration of artificial intelligence (AI) into digital histopathology has the potential to improve the accuracy and efficiency of metastatic cancer diagnosis. We evaluated LYDIA (LYmph noDe assIstAnt), an AI-based decision-support system, for both standalone diagnostic performance and its impact on histopathologists' workflow, diagnostic accuracy, and resource utilization. LYDIA was evaluated on a blinded dataset of 366 whole-slide images (WSIs) from breast, colorectal, lung, and skin cancers. Standalone performance demonstrated excellent discrimination, achieving ROC-AUC values of 0.995, 0.963, 0.973, and 0.983 for breast, colorectal, lung, and skin cancers, respectively. Clinical utility was further assessed in a multi-reader study involving four experienced histopathologists interpreting 105 WSIs with and without AI assistance. AI-assisted diagnosis significantly reduced time-to-diagnosis across all metastasis sizes, with a maximum 1.59-fold acceleration for micro-metastases, corresponding to a mean time saving of 26.5 s per WSI. LYDIA also improved diagnostic sensitivity from 77.3% to 87.3%. These findings demonstrate that LYDIA can enhance both the efficiency and accuracy of lymph node metastasis detection while reducing diagnostic workload and the need for ancillary testing. Beyond improving routine pathology workflows, the system's rapid inference capabilities and human-expert level performance may support future intraoperative diagnostic applications, enabling timely and automatic or semi-automatic assessment of nodal status to inform surgical decision-making. The resulting reductions in diagnostic time and ancillary testing costs have the potential to improve healthcare resource utilization and patient care, mainly in soft tissue reconstruction and surgical repair decisions.
Indexed as
Identifiers
What OpenQuestion holds
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.