Professor Chi Ming Che, Zhou Guangzhao Professor in Natural Sciences and Chair Professor of Chemistry, Department of Chemistry, Faculty of Science, The University of Hong Kong (HKU), and the Laboratory for Synthetic Chemistry and Chemical Biology Limited (LSCCB), and Dr Wei Liu, Department of Chemistry, Faculty of Science, HKU, and LSCCB, have developed an innovative portable AI-enabled optical sensing device that enables rapid, non-invasive cancer risk detection using just a saliva sample. This innovative technology integrates advanced chemical sensing with artificial intelligence and was awarded a Gold Medal with Congratulations of the Jury at the 51st International Exhibition of Inventions of Geneva (2026), recognising its scientific excellence and strong potential for practical application.   

A new study published in Research describes a deep-learning system designed to assist lumbar spine MRI at the point of image acquisition rather than only after scanning. The system, named Lumbar VNet Pro (LVP), was embedded directly into the MRI workflow and evaluated in a multicenter clinical setting. According to the study, the platform supports automated anatomical localization, scan-plane optimization, and real-time structural analysis during MRI scanning, offering a possible route toward more standardized and intelligent imaging workflows.

Despite extensive development of flexible pressure sensors, it is still difficult for them to simultaneously achieve high precision and a large response to subtle pressures. To address these challenges, this work demonstrates a flexible pressure sensing platform that features the reduced graphene oxide aerogel sandwiched between a polydimethylsiloxane encapsulation layer and a thin polyimide film with interdigital electrodes. The resulting pressure sensor exhibits a high sensitivity of 698.96 kPa⁻¹ and a low limit of detection (~ 1 Pa), and outstanding stability over 20,000 loading/unloading cycles. Besides monitoring various physiological signals and human motions, the flexible pressure sensors can be configured into an array layout as a smart artificial electronic skin to recognize the spatial pressure distribution. The flexible pressure sensor can also be integrated with signal processing and wireless communication modules as a teleoperation system for gesture recognition, force feedback control, and kitchen food recognition, highlighting future potential toward smart robotics and human–machine interfaces.

ACM, the Association for Computing Machinery, today named Monika Henzinger of the Institute of Science and Technology Austria (ISTA) as the 2026-2027 ACM Athena Lecturer. Henzinger is recognized for outstanding contributions to the fields of dynamic graph algorithms and web algorithms, and for dedicated mentoring and service to these communities.

Small, flexible, wireless device gently adheres to the chest to measure multiple physiological signals at once. By tracking heart rate and rhythm, breathing rate and depth, sweat response, blood flow and temperature, device captures a whole-body ‘view’ of stress. Machine learning algorithms analyze patterns associated with stress in real time. Device could help clinicians detect stress and discomfort in babies, the elderly and critically ill or sedated patients who cannot communicate.

Artificial intelligence models that automatically label organs and anatomical structures in medical images are increasingly used to study large public imaging datasets, but comparing these tools is difficult when no expert reference annotations exist. A recent study developed an open-source framework for evaluating and comparing AI-based anatomy segmentation models using inter-model agreement rather than ground truth. Applying the approach to six widely used segmentation models and chest CT scans from the National Lung Screening Trial, the researchers normalized all model outputs to follow standard lexicon, and stored the result in a common medical imaging format. Normalized representation made it possible to visualize and compare model outputs, and analyze how consistently each model segmented key structures such as the lungs, heart, ribs, vertebrae, and sternum. The results show strong agreement across models for lung segmentation, but substantial inconsistencies and systematic errors for bones and some cardiac structures, particularly among models trained on similar data. By combining quantitative agreement metrics with interactive visualization tools, the framework helps identify reliable models and flag problem cases, offering a practical way to guide model selection and large-scale reuse of AI-generated annotations.