AIMC Topic: Humans

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Integrating intratumoral and peritumoral radiomics with deep transfer learning for DCE-MRI breast lesion differentiation: A multicenter study comparing performance with radiologists.

European journal of radiology
PURPOSE: To conduct the fusion of radiomics and deep transfer learning features from the intratumoral and peritumoral areas in breast DCE-MRI images to differentiate between benign and malignant breast tumors, and to compare the diagnostic accuracy o...

Clinical domain knowledge-derived template improves post hoc AI explanations in pneumothorax classification.

Journal of biomedical informatics
OBJECTIVE: Pneumothorax is an acute thoracic disease caused by abnormal air collection between the lungs and chest wall. Recently, artificial intelligence (AI), especially deep learning (DL), has been increasingly employed for automating the diagnost...

Frequency and characteristics of errors by artificial intelligence (AI) in reading screening mammography: a systematic review.

Breast cancer research and treatment
PURPOSE: Artificial intelligence (AI) for reading breast screening mammograms could potentially replace (some) human-reading and improve screening effectiveness. This systematic review aims to identify and quantify the types of AI errors to better un...

The Improved Biometric Identification of Keystroke Dynamics Based on Deep Learning Approaches.

Sensors (Basel, Switzerland)
In an era marked by escalating concerns about digital security, biometric identification methods have gained paramount importance. Despite the increasing adoption of biometric techniques, keystroke dynamics analysis remains a less explored yet promis...

Ultra-Wide Band Radar Empowered Driver Drowsiness Detection with Convolutional Spatial Feature Engineering and Artificial Intelligence.

Sensors (Basel, Switzerland)
Driving while drowsy poses significant risks, including reduced cognitive function and the potential for accidents, which can lead to severe consequences such as trauma, economic losses, injuries, or death. The use of artificial intelligence can enab...

Identification of Respiratory Pauses during Swallowing by Unconstrained Measuring Using Millimeter Wave Radar.

Sensors (Basel, Switzerland)
Breathing temporarily pauses during swallowing, and the occurrence of inspiration before and after these pauses may increase the likelihood of aspiration, a serious health problem in older adults. Therefore, the automatic detection of these pauses wi...

Accurate Identification of Spatial Domain by Incorporating Global Spatial Proximity and Local Expression Proximity.

Biomolecules
Accurate identification of spatial domains is essential in the analysis of spatial transcriptomics data in order to elucidate tissue microenvironments and biological functions. However, existing methods only perform domain segmentation based on local...

Deep learning models for predicting the survival of patients with hepatocellular carcinoma based on a surveillance, epidemiology, and end results (SEER) database analysis.

Scientific reports
Hepatocellular carcinoma (HCC) is a common malignancy with poor survival and requires long-term follow-up. Hence, we collected information on patients with Primary Hepatocellular Carcinoma in the United States from the Surveillance, Epidemiology, and...

Deep learning restores speech intelligibility in multi-talker interference for cochlear implant users.

Scientific reports
Cochlear implants (CIs) do not offer the same level of effectiveness in noisy environments as in quiet settings. Current single-microphone noise reduction algorithms in hearing aids and CIs only remove predictable, stationary noise, and are ineffecti...

Motor assessment of X-linked dystonia parkinsonism via machine-learning-based analysis of wearable sensor data.

Scientific reports
X-linked dystonia parkinsonism (XDP) is a neurogenetic combined movement disorder involving both parkinsonism and dystonia. Complex, overlapping phenotypes result in difficulties in clinical rating scale assessment. We performed wearable sensor-based...