Recent years have ushered in a transformative era in in vitro modeling with the advent of organoids, three-dimensional structures derived from stem cells or patient tumor cells. Still, fully harnessing the potential of organoids requires advanced imaging technologies and analytical tools to quantitatively monitor organoid growth. Optical coherence tomography (OCT) is a promising imaging modality for organoid analysis due to its high-resolution, label-free, non-destructive, and real-time 3D imaging capabilities, but accurately identifying and quantifying organoids in OCT images remain challenging due to various factors. Here, we propose an automatic deep learning-based pipeline with convolutional neural networks that synergistically includes optimized preprocessing steps, the implementation of a state-of-the-art deep learning model, and ad-hoc postprocessing methods, showcasing good generalizability and tracking capabilities over an extended period of 13 days. The proposed tracking algorithm thoroughly documents organoid evolution, utilizing reference volumes, a dual branch analysis, key attribute evaluation, and probability scoring for match identification. The proposed comprehensive approach enables the accurate tracking of organoid growth and morphological changes over time, advancing organoid analysis and serving as a solid foundation for future studies for drug screening and tumor drug sensitivity detection based on organoids.

This study introduces a machine learning approach to automate muscle ultrasound analysis, aiming to improve objectivity and efficiency in segmentation, classification, and Heckmatt grading. We analyzed a dataset of 25,005 B-mode images from 290 participants (110 FSHD patients) acquired using a single Esaote ultrasound scanner with a standardized protocol. Manual segmentation and Heckmatt grading by experienced observers served as ground truth. K-Net was utilized for simultaneous muscle segmentation and classification. Heckmatt scoring was approached with texture analysis, using a modified scale with three classes (Normal, Uncertain, Abnormal). Radiomics features were extracted using PyRadiomics and automatic scoring was performed using XGBoost, incorporating explainability through SHAP analysis. K-Net demonstrated high accuracy in skeletal muscle classification and segmentation, with Intersection over Union ranging from 73.40 to 74.03 across folds. Heckmatt's grading achieved an Area Under Curve of 0.95, 0.87, and 0.97 for classes Normal, Uncertain, and Abnormal. SHAP analysis highlighted histogram-based features as critical for visual scoring. This study proposes and validates an automatic pipeline for muscle ultrasound analysis, leveraging machine learning for segmentation, classification, and quantitative Heckmatt grading. Automating the visual assessment of muscle ultrasound images improves the objectivity and efficiency of muscle ultrasound, supporting clinical decision-making.

Characterization of the optic nerve through measurement of optic nerve diameter (OND) and optic nerve sheath diameter (ONSD) using transorbital sonography (TOS) has proven to be a useful tool for the evaluation of intracranial pressure (ICP) and multiple neurological conditions. We describe a deep learning-based system for automatic characterization of the optic nerve from B-mode TOS images by automatic measurement of the OND and ONSD. In addition, we determine how the signal-to-noise ratio in two different areas of the image influences system performance. A UNet was trained as the segmentation model. The training was performed on a multidevice, multicenter data set of 464 TOS images from 110 subjects. Fivefold cross-validation was performed, and the training process was repeated eight times. The final prediction was made as an ensemble of the predictions of the eight single models. Automatic OND and ONSD measurements were compared with the manual measurements taken by an expert with a graphical user interface that mimics a clinical setting. A Dice score of 0.719 ± 0.139 was obtained on the whole data set merging the test folds. Pearson's correlation was 0.69 for both OND and ONSD parameters. The signal-to-noise ratio was found to influence segmentation performance, but no clear correlation with diameter measurement performance was determined. The developed system has a good correlation with manual measurements, proving that it is feasible to create a model capable of automatically analyzing TOS images from multiple devices. The promising results encourage further definition of a standard protocol for the automatization of the OND and ONSD measurement process using deep learning-based methods. The image data and the manual measurements used in this work will be available at 10.17632/kw8gvp8m8x.1.

Artificial intelligence (AI) is rapidly reshaping gastrointestinal (GI) surgery by enhancing decision-making, intraoperative performance, and postoperative management. The integration of AI-driven systems is enabling more precise, data-informed, and personalized surgical interventions. This review provides a state-of-the-art overview of AI applications in GI surgery, organized into four key domains: surgical simulation, surgical computer vision, surgical data science, and surgical robot autonomy. A comprehensive narrative review of the literature was conducted, identifying relevant studies of technological developments in this field. In the domain of surgical simulation, AI enables virtual surgical planning and patient-specific digital twins for training and preoperative strategy. Surgical computer vision leverages AI to improve intraoperative scene understanding, anatomical segmentation, and workflow recognition. Surgical data science translates multimodal surgical data into predictive analytics and real-time decision support, enhancing safety and efficiency. Finally, surgical robot autonomy explores the progressive integration of AI for intelligent assistance and autonomous functions to augment human performance in minimally invasive and robotic procedures. Surgical AI has demonstrated significant potential across different domains, fostering precision, reproducibility, and personalization in GI surgery. Nevertheless, challenges remain in data quality, model generalizability, ethical governance, and clinical validation. Continued interdisciplinary collaboration will be crucial to translating AI from promising prototypes to routine, safe, and equitable surgical practice.

Purpose: Automating suturing in robotic-assisted surgery offers significant benefits including enhanced precision, reduced operative time, and alleviated surgeon fatigue. Achieving this requires robust computer vision (CV) models. Still, their development is hindered by the scarcity of task-specific datasets and the complexity of acquiring and annotating real surgical data. This work addresses these challenges using a sim-to-real approach to create synthetic datasets and a data-driven methodology for model training and evaluation. Methods: Existing 3D models of Da Vinci tools were modified and new models–needle and tissue cuts–were created to account for diverse data scenarios, enabling the generation of three synthetic datasets with increasing realism using Unity and the Perception package. These datasets were then employed to train several YOLOv8-m models for object detection to evaluate the generalizability of synthetic-trained models in real scenarios and the impact of dataset realism on model performance. Additionally, a real-time instance segmentation model was developed through a hybrid training strategy combining synthetic and a minimal set of real images. Results: Synthetic-trained models showed improved performance on real test sets as training dataset realism increased, but realism levels remained insufficient for complete generalization. Instead, the hybrid approach significantly increased performance in real scenarios. Indeed, the hybrid instance segmentation model exhibited real-time capabilities and robust accuracy, achieving the best Dice coefficient (0.92) with minimal dependence on real training data (30–50 images). Conclusions: This study demonstrates the potential of sim-to-real synthetic datasets to advance robotic suturing automation through a simple and reproducible framework. By sharing 3D models, Unity environments and annotated datasets, this work provides resources for creating additional images, expanding datasets, and enabling fine-tuning or semi-supervised learning. By facilitating further exploration, this work lays a foundation for advancing suturing automation and addressing task-specific dataset scarcity.