Artificial Intelligence Medical Compendium

OBJECTIVE: To develop and validate a multimodal deep-learning classifier trained on stroboscopic image, voice, and clinicodemographic data, differentiating between three different vocal fold (VF) states: healthy (HVF), unilateral paralysis (UVFP), an... read more 

BACKGROUND: Multi-center imaging studies create large-scale data that are useful for identifying pathological patterns and robust training of deep learning models. However, variation due to site and scanner differences can confound analyses, emphasiz... read more 

Metal-oxide semiconductor (MOS)-based synaptic transistors are promising candidates for highly integrated neuromorphic chips. Ferroelectrics and electrolytes have been extensively studied, but they have not satisfied the scalability and integration o... read more 

Photodynamic therapy (PDT) is an emerging approach for tumor treatment, valued for its noninvasive and stimuli-responsive properties. However, its therapeutic efficacy is often constrained by unintended damage to healthy tissues, largely due to the s... read more 

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With the increasing deployment of intelligent robotics in medical rehabilitation and elderly care, sensing modalities and functionalities eventually become a critical issue in achieving comprehensive awareness regarding operational tasks. Except mult... read more 

Traditional Ames tests for chemical mutagenicity are slow, costly, and often yield inconsistent results between in vitro and in vivo assays, hindering high-throughput safety screening. To address these limitations, we propose AMPred-LWN, a multimodal... read more 

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PURPOSE: This study aimed to develop deep learning (DL) models for CT-free attenuation correction and Monte Carlo-based scatter correction in 99mTc-macroagregated albumin (99mTc-MAA) SPECT imaging, with the goal of enhancing quantitative accuracy for... read more