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Background Intra-hospital transport of critically ill, mechanically ventilated patients is associated with significant physiological risks. Adaptive Minute Ventilation (AMV) is a closed-loop ventilation mode that automatically maintains target minute ventilation. While AMV has shown promise in intensive care unit settings, its performance during intrahospital transport has not yet been evaluated. This trial aims to assess the safety and efficacy of AMV compared with conventional ventilation strategies during patient transport. Methods This is a single-centre, open-label, randomized controlled non-inferiority trial conducted at Peking Union Medical College Hospital. Fifty adult patients receiving invasive mechanical ventilation and requiring transport will be randomly assigned (1:1) to either AMV or their pre-transport ventilation mode. The primary endpoint is the change in arterial carbon dioxide pressure (PaCO₂) from 30 min before to 10 min after transport. Secondary outcomes include changes in oxygenation index (PaO₂/FiO₂), peak airway pressure, dynamic compliance, and rapid shallow breathing index. Randomization will be performed using permuted block randomization. Both intention-to-treat and per-protocol analyses will be conducted. Missing data will be handled using multiple imputation. The study protocol has been reviewed and approved by the Ethics Committee of Peking Union Medical College Hospital (Approval No. I-25PJ0781). It was registered in the Chinese Clinical Trial Registry (ChiCTR2500109659) on 23 September 2025 as a retrospective registration. Discussion This trial will be the first randomized study to evaluate AMV during intrahospital transport of critically ill patients. The findings will address an important evidence gap in transport ventilation strategies, potentially informing clinical practice on whether AMV provides a safe and effective alternative to conventional modes. By employing a non-inferiority design and standardized transport protocols, the study seeks to generate robust and clinically relevant evidence despite its single-centre setting and relatively small sample size. Trial registration Approved by the Ethics Committee of Peking Union Medical College Hospital (Approval No. I-25PJ0781). Trial registration: ChiCTR, ChiCTR2500109659. Registered 23 September 2025, https://www.chictr.org.cn/hvshowproject.html?id=284847 .

This study proposes a deep learning-based framework for automated segmentation of brain regions and classification of amyloid positivity using positron emission tomography (PET) images alone, without the need for structural MRI or CT. A 3D U-Net architecture with four layers of depth was trained and validated on a dataset of 200 F18-florbetapir amyloid-PET scans, with an 130/20/50 train/validation/test split. Segmentation performance was evaluated using Dice similarity coefficients across 30 brain regions, with scores ranging from 0.45 to 0.88, demonstrating high anatomical accuracy, particularly in subcortical structures. Quantitative fidelity of PET uptake within clinically relevant regions. Precuneus, prefrontal cortex, gyrus rectus, and lateral temporal cortex was assessed using normalized root mean square error, achieving values as low as 0.0011. Furthermore, the model achieved a classification accuracy of 0.98 for amyloid positivity based on regional uptake quantification, with an area under the ROC curve (AUC) of 0.99. These results highlight the model's potential for integration into PET only diagnostic pipelines, particularly in settings where structural imaging is not available. This approach reduces dependence on coregistration and manual delineation, enabling scalable, reliable, and reproducible analysis in clinical and research applications. Future work will focus on clinical validation and extension to diverse PET tracers including C11 PiB and other F18 labeled compounds.