Artificial Intelligence Medical Compendium

BACKGROUND: Pulse shape index (PSI) is a novel artificial intelligence-supported parameter that evaluates the pressure-volume compensatory reserve of the craniospinal system through intracranial pressure (ICP) pulse waveform classification. This stud... read more 

OBJECTIVES: Accurate detection of breast lesion type is crucial for optimizing treatment; however, due to the limited precision of current diagnostic methods, biopsies are often required. To address this limitation, we proposed radio frequency time s... read more 

BACKGROUND: Transrectal in-bore MR-guided biopsy (MRGB) is accurate but time-consuming, limiting clinical throughput. Faster imaging could improve workflow and enable real-time instrument tracking. Existing acceleration methods often use simulated da... read more 

AIM: To compare the accuracy and design time of artificial intelligence (AI)-generated and manually designed (MD) surgical pathways for osteotomies and root-end resections in dynamic navigation (DN)-aided endodontic microsurgery (EMS). METHODOLOGY: F... read more 

OBJECTIVES: While the implications of AI for health care are frequently discussed, AI's relationship to abortion has received limited attention. This paper surveys research on AI and abortion to date, adapts AI ethics principles for abortion contexts... read more 

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As a key feedstock for sustainable bioenergy, microalgae require precise regulation of lipid synthesis and accurate detection methods. To efficiently induce lipid accumulation while minimizing chemical intervention, this study employed multi-frequenc... read more 

This work proposes a black-box purely data-driven strategy for structural damage identification concerning localized damaged regions in plate-like structures. The proposed strategy is based on Convolution Neural Networks in the framework of a supervi... read more 

Chemiluminescent (CL) biosensors have rapidly emerged as indispensable analytical tools within the multifaceted domains of pharmaceutical research, development, and clinical diagnostics. This prominence stems from their intrinsic attributes such as h... read more 

The small sphere and large margin approach (SSLM) is a representative learning algorithm for handling imbalanced data classification problems. However, it is only effective for the vector data, and not suitable for the tensor data. How to build a nov... read more