Weed adaptability to environmental conditions poses a major challenge in agricultural production, often leading to yield reduction or even complete crop loss. Effective weed detection requires algorithms tailored to the unique visual and biological c... read more
Collaborative robots increasingly share workspaces with humans, making the predictability of robot actions critical for efficient and safe coordination. Predictive visual cues, such as gaze or arrows, may reduce uncertainty, yet their effectiveness a... read more
Cancer immunology, immunotherapy : CII
May 4, 2026
PURPOSE: The prognostic significance of tumor-infiltrating lymphocytes (TILs) in colorectal cancer (CRC) is well established; however, existing approaches inadequately capture their spatial distribution. We investigated the prognostic implications of... read more
Accurate classification of renal masses before treatment is crucial for therapeutic decision-making and patient outcome. This study developed and validated Multi-Phase Attention Network (MPANet), a multimodal deep learning model integrating multiphas... read more
Journal of imaging informatics in medicine
May 4, 2026
This study aimed to assess the diagnostic performance of the Brazilian-developed artificial intelligence system DIO Inteligência® for automatic detection and classification of primary and permanent teeth in panoramic radiographs of patients in mixed ... read more
There is a significant global health need to translate more in vitro diagnostic tests from clinical laboratories to field-based applications, including point-of-care and self-administered test formats. These applications typically require smaller sam... read more
BACKGROUND: Mood disorders after aneurysmal subarachnoid haemorrhage (aSAH) are common. Meanwhile, mood disorders are also common after intensive care for any reason, and whether aSAH confers an excess risk remains unknown. METHODS: In this retrospec... read more
AIM: To develop and validate a deep learning-based AI system for the dynamic, real-time differentiation of benign and malignant gastric ulcers during endoscopy, with the goal of enhancing diagnostic precision. METHODS: This was a multicenter, retrosp... read more
BACKGROUND: Machine Learning (ML) models have achieved outstanding performance in predicting post-surgical survival. However, the "black-box" nature of ML models restricts their clinical application. This study aims to develop and validate a clinical... read more
BACKGROUND: Dengue transmission in Indonesia is shaped by interacting climatic, environmental, and socio-demographic factors, yet most forecasting systems remain static and vulnerable to data shifts. There is a critical need for adaptive, data-driven... read more
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