Results 161 to 170 of about 159,034 (226)

End‐to‐End Sensing Systems for Breast Cancer: From Wearables for Early Detection to Lab‐Based Diagnosis Chips

Advanced Materials Technologies, EarlyView.
This review explores advances in wearable and lab‐on‐chip technologies for breast cancer detection. Covering tactile, thermal, ultrasound, microwave, electrical impedance tomography, electrochemical, microelectromechanical, and optical systems, it highlights innovations in flexible electronics, nanomaterials, and machine learning.
Neshika Wijewardhane, Lyn I. Jones, Richard M. Martin, Helen Winter, Faezeh Arab Hassani   +4 more
wiley   +1 more source

Development and validation of an interpretable machine learning model for predicting medium-to-giant coronary aneurysms in Kawasaki disease. [PDF]

Front Immunol
Zhang J, Li J, Dong J, You T, Hou M, Liu Y, He Y, Liu P, Wang Y, Li L, Ma S, Lv H, Huang H.   +12 more
europepmc   +1 more source

Transducers Across Scales and Frequencies: A System‐Level Framework for Multiphysics Integration and Co‐Design

Advanced Materials Technologies, EarlyView.
Transducers convert physical signals into electrical and optical representations, yet each mechanism is bounded by intrinsic trade‐offs across bandwidth, sensitivity, speed, and energy. This review maps transduction mechanisms across physical scale and frequency, showing how heterogeneous integration and multiphysics co‐design transform isolated ...
Aolei Xu, Mengyao Xiao, Zhouli Sui, Yinpeng Wang, Daoye Zheng, Yuxin Liu, Luwei Wang, Huajun Liu, Chengkuo Lee   +8 more
wiley   +1 more source

An interpretable machine learning model for predicting prognosis of medulloblastoma integrating genetic and clinical features. [PDF]

Commun Med (Lond)
Su Y, Deng K, Chen X, Feng Z, Wang D, Daniels C, Koh HY, Gonzalez RD, Suzuki H, Miyauchi T, Liu F, Wang W, Li J, Li S, Chen R, Qiu X, Li C, Jiang T, Taylor MD, Zhang J, Liu H, Tian Y.   +21 more
europepmc   +1 more source

Characterization of Droplet Formation in Ultrasonic Spray Coating: Influence of Ink Formulation Using Phase Doppler Anemometry and Machine Learning

Advanced Materials Technologies, EarlyView.
This study explores how machine learning models, trained on small experimental datasets obtained via Phase Doppler Anemometry (PDA), can accurately predict droplet size (D32) in ultrasonic spray coating (USSC). By capturing the influence of ink complexity (solvent, polymer, nanoparticles), power, and flow rate, the model enables precise droplet control
Pieter Verding, Danny E. P. Vanpoucke, Yunus T. Aksoy, Tobias Corthouts, Maria R. Vetrano, Wim Deferme   +5 more
wiley   +1 more source

Interpretable machine learning for accessible dysphagia screening and staging in older adults. [PDF]

iScience
Dai Y, Cai J, Gong Z, Niu C, Yu W, Wang H, Zhang Y.   +6 more
europepmc   +1 more source

Respiratory Signal Processing and Analysis Using Flexible Capacitive Sensor Data

Advanced Materials Technologies, EarlyView.
Capacitive pressure sensors based on poly(glycerol sebacate) (PGS) substrates are developed for continuous, non‐invasive respiratory monitoring. Integrated with a signal processing algorithm, they enable accurate tracking of thoracic expansion and retraction.
Bernardo A. Vicente, Raquel Sebastião, Alda Marques, Vitor Sencadas   +3 more
wiley   +1 more source

Unlocking the Potential of MXene‐Based Electrochemical Biosensors: A Review of Biofunctionalization Strategies and Biosensing Principles

Advanced Materials Technologies, EarlyView.
ABSTRACT Electrochemical biosensors enable the accurate and timely detection of clinical biomarkers, improving healthcare and precision medicine. MXene nanosheets, a class of 2D transition metal carbides, nitrides, and carbonitrides, are promising materials for developing next‐generation electrochemical biosensors due to their unique physicochemical ...
Muhsin Ali, Sharat Chandra Barman, Isobel Steer, Aya H. Alshareef, Dana Alsulaiman   +4 more
wiley   +1 more source

Interpretable machine learning for prognostic prediction in critically ill patients with coronary artery disease: a multicenter study. [PDF]

Front Med (Lausanne)
Yang S, Zhang S, Ma L, Zeng J, Wang M, Huang S, Zhang X, Liang X, Zhu M.   +8 more
europepmc   +1 more source