Abstract Background Three-dimensional (3D) reconstruction of ossicular chain and bony labyrinth based on temporal bone high resolution computed tomography (HRCT) is useful for diagnosis and treatment guidance of middle and inner ear diseases. However, these structures are small and irregular, making manual reconstruction time-consuming. Purpose To develop and validate an artificial intelligence (AI) model based on semi-supervised learning for automated 3D reconstruction of ossicular chain and bony labyrinth on HRCT images. Methods HRCT images from 304 ears of consecutive 152 patients retrospectively collected from a single center were randomly divided into training (246 ears), validation (28 ears) and internal test (30 ears) cohorts for model development. A novel semi-supervised ear bone segmentation framework was used to train the AI model, and its performance was evaluated by Dice similarity coefficients. The trained algorithm was applied to a temporally independent test dataset of 30 ears of 15 patients from the same center for comparison with manual 3D reconstruction for processing time, target volume and visual assessment of segmentation. Results The AI model demonstrated a Dice score of 0.948 (95% CI: 0.940, 0.955) for the internal and 0.979 (95% CI: 0.973, 0.986) for the temporally independent test sets. In the latter dataset, the AI model required 2% or less processing time of manual 3D reconstruction for each ear (17.7 seconds ± 10.1 vs 1080.5 seconds ± 149.8; P < .001), and had an accuracy comparable to human experts in the volume and visual assessment of segmentation targets (P = .237-1.000). In a subgroup analysis, the model achieved accurate segmentation (Dice scores of 0.98-0.99) across various diseases (e.g. otitis media, mastoiditis, otosclerosis, middle and inner ear malformations, and Ménière’s disease). Conclusion The AI model enables robust, efficient and accurate 3D reconstruction for the small structures such as ossicular chain and bony labyrinth on HRCT images.