ArticleUpdates in surgery2026
Training the diagnostic artificial intelligence in thyroid sonography: how well is deep learning truly learning?
Article in Updates in surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThe use of artificial intelligence (AI) for TI-RADS classification in neck sonography for the assessment of thyroid nodules has been proven beneficial. We evaluated the learning curve of such AI in a real-world setting.
methodsBetween 03/2023 and 06/2024, 110 patients with 176 thyroid nodules were examined and classified according to ACR TI-RADS classification using 3D-ultrasound PIUR tUS Infinity software. After software training and update, the study was repeated examining 133 patients with 228 nodules (03/2023 until 10/2024). Every AI-based TI-RADS evaluation was compared to that of an experienced endocrine surgeon (assessor), unaware of the software results.
resultsFirst phase: AI-supported TI-RADS classification corresponded to the assessor's in 128/176 (73%) of examined nodules; second phase: correspondence in 210/227 (92.6%). Re-evaluating the initial 110 patients after software update, more nodules (194 vs. 176) were correctly assessed. In the first phase, AI "misinterpreted" in 36/176 (20%) cases nodules with microcalcifications or echogenic foci (leading to 1-3 points differences to accessor). After update, only 11/227 (4.8%) such nodules remained and relevant differences to the accessor were found in only 6/227 (2.6%) unusual cases (autoimmune thyroiditis, non-descended thymus, hemorrhaged cyst), compared to the initial 7%.
conclusionsAfter several rounds of deep learning, a significant improvement in correct ACR TI-RADS classification was demonstrated, especially the assessment of nodules within conglomerates. The AI-supported ultrasound is already a solid tool in the diagnosis of true thyroid nodules in non-inflamed tissue, but cannot yet replace an experienced clinician in complex or unusual cases; however, the fast learning curve is encouraging.
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.