Deep learning-based diagnostic models for bone lesions: is current research ready for clinical translation?

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

3 citing papers in PubMed.

1. Bone-CNN: A Lightweight Deep Learning Architecture for Multi-Class Classification of Primary Bone Tumours in Radiographs.Biomedicines · 2026
   Article
2. Bone reporting and data system on CT (Bone-RADS-CT): a validation study by four readers on 328 cases from three local and two public databases.Insights into imaging · 2025
   Article
3. Bone Reporting and Data System on MRI (Bone-RADS-MRI): a validation study by four readers on 275 cases from three local and two public databases.Insights into imaging · 2025
   Article

Corrections and comments

• Comment on
  Automatic detection, segmentation, and classification of primary bone tumors and bone infections using an ensemble multi-task deep learning framework on multi-parametric MRIs: a multi-center study.2024

Authors and funding

1 author.

Funding

Abstract

PubMed holds no abstract for this paper.

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