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cs.CLcs.MAMohamed Sobhi Jabal (1), Jikai Zhang (2, 3) et al.
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Mar 23, 2026
This paper tackles the challenge of automating BT-RADS (Brain Tumor Reporting and Data System) classification for post-treatment glioma MRI surveillance. BT-RADS requires integrating complex information: volumetric tumor changes, medication effects (steroids, bevacizumab), and radiation timing. The authors propose an end-to-end pipeline combining CNN-based tumor segmentation with a multi-agent LLM system to extract clinical variables from unstructured notes and apply algorithmic scoring logic. This matters because manual BT-RADS scoring is error-prone, with prior studies showing substantial inter-reader variability and inconsistent application of clinical context.
The Brain Tumor Reporting and Data System (BT-RADS) standardizes post-treatment MRI response assessment in patients with diffuse gliomas but requires complex integration of imaging trends, medication effects, and radiation timing. This study evaluates an end-to-end multi-agent large language model (LLM) and convolutional neural network (CNN) system for automated BT-RADS classification. A multi-agent LLM system combined with automated CNN-based tumor segmentation was retrospectively evaluated on 509 consecutive post-treatment glioma MRI examinations from a single high-volume center. An extractor agent identified clinical variables (steroid status, bevacizumab status, radiation date) from unstructured clinical notes, while a scorer agent applied BT-RADS decision logic integrating extracted variables with volumetric measurements. Expert reference standard classifications were established by an independent board-certified neuroradiologist. Of 509 examinations, 492 met inclusion criteria. The system achieved 374/492 (76.0%; 95% CI, 72.1%-79.6%) accuracy versus 283/492 (57.5%; 95% CI, 53.1%-61.8%) for initial clinical assessments (+18.5 percentage points; P<.001). Context-dependent categories showed high sensitivity (BT-1b 100%, BT-1a 92.7%, BT-3a 87.5%), while threshold-dependent categories showed moderate sensitivity (BT-3c 74.8%, BT-2 69.2%, BT-4 69.3%, BT-3b 57.1%). For BT-4, positive predictive value was 92.9%. The multi-agent LLM system achieved higher BT-RADS classification agreement with expert reference standard compared to initial clinical scoring, with high accuracy for context-dependent scores and high positive predictive value for BT-4 detection.
cs.ROcs.LGcs.MAEbasa Temesgen, Nathnael Minyelshowa, Lebsework Negash
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Mar 22, 2026
This paper proposes a multi-UAV architecture for autonomous precision agriculture that combines centralized mission planning with decentralized execution control. It integrates coverage path planning, battery-aware task allocation, CNN-based image processing, and battery swapping stations to enable end-to-end farm monitoring. The work targets large-scale agricultural operations with minimal human intervention, claiming advantages in fault-tolerance, scalability, and user-friendliness.
The use of unmanned aerial vehicles (UAVs) in precision agriculture has seen a huge increase recently. As such, systems that aim to apply various algorithms on the field need a structured framework of abstractions. This paper defines the various tasks of the UAVs in precision agriculture and model them into an architectural framework. The presented architecture is built on the context that there will be minimal physical intervention to do the tasks defined with multiple coordinated and cooperative UAVs. Various tasks such as image processing, path planning, communication, data acquisition, and field mapping are employed in the architecture to provide an efficient system. Besides, different limitation for applying Multi-UAVs in precision agriculture has been considered in designing the architecture. The architecture provides an autonomous end-to-end solution, starting from mission planning, data acquisition and image processing framework that is highly efficient and can enable farmers to comprehensively deploy UAVs onto their lands. Simulation and field tests shows that the architecture offers a number of advantages that include fault-tolerance, robustness, developer and user-friendliness.