ArticleFrontiers in bioengineering and biotechnology2026
Intelligent diagnosis of ossicular chain malformations on CT: development and clinical efficacy of a cascaded AI framework.
Article in Frontiers in bioengineering and biotechnology, 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
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: The aim of this study was to develop and validate a cascaded artificial intelligence (AI) framework using a segmentation-discrimination strategy for automated detection of ossicular chain malformations (OCM) on computed tomography scans. Methods: Patients diagnosed with OCM between January 2009 and January 2023, along with healthy controls, were retrospectively enrolled. A coarse-to-fine nnU-Net framework was applied for automated segmentation of the auditory ossicles. Separate discrimination models were developed for the malleus, incus, and stapes, and subsequently integrated to generate a patient-level model. The performance of the proposed framework was compared with deep learning networks, a volume-threshold algorithm, and by radiologists. The temporal external validation was conducted on data from multiple sites within Shaanxi Province, using the same CT vendors. The discriminatory capacity of each ossicular-level model was assessed using area under the curve (AUC), sensitivity, specificity, and accuracy. Results: A total of 2,462 temporal bone computed tomography scans (training set, n = 1,302; test set, n = 556; validation set, n = 604) were analyzed. The cascaded network demonstrated AUC values of 0.962, 0.930, and 0.931 for the malleus, incus, and stapes, respectively. At the patient level, accuracy, sensitivity, and specificity were 0.874, 0.949, and 0.800, respectively, surpassing the performance of ResNet, DenseNet, the volume-threshold algorithm, and junior radiologists, while equaling that of senior radiologists. In temporal validation, the network maintained robust performance, with AUC values of 0.972, 0.973, and 0.963, and patient-level metrics of 0.960 (accuracy), 0.974 (sensitivity), and 0.947 (specificity). Conclusion: The cascaded AI framework using a segmentation-discrimination strategy demonstrated high performance in identifying patients with diverse types of OCMs on computed tomography scans. This approach has the potential to support radiologists in the diagnostic evaluation of OCMs.
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.