AIMC Topic: Humans

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LGEANet: LSTM-global temporal convolution-external attention network for respiratory motion prediction.

Medical physics
PURPOSE: To develop a deep learning network that treats the three-dimensional respiratory motion signals as a whole and considers the inter-dimensional correlation between signals of different directions for accurate respiratory tumor motion predicti...

APLUS: A Python library for usefulness simulations of machine learning models in healthcare.

Journal of biomedical informatics
Despite the creation of thousands of machine learning (ML) models, the promise of improving patient care with ML remains largely unrealized. Adoption into clinical practice is lagging, in large part due to disconnects between how ML practitioners eva...

Algorithmic encoding of protected characteristics in chest X-ray disease detection models.

EBioMedicine
BACKGROUND: It has been rightfully emphasized that the use of AI for clinical decision making could amplify health disparities. An algorithm may encode protected characteristics, and then use this information for making predictions due to undesirable...

Single-port robot-assisted perineal radical prostatectomy with the da Vinci XI system: initial experience and learning curve using the cumulative sum method.

World journal of surgical oncology
BACKGROUND: To evaluate the early functional and oncological outcomes of single-port robot-assisted perineal radical prostatectomy (sp-pRARP) using the da Vinci XI system and analyze its learning curve using the cumulative sum (CUSUM) method.

A method based on interpretable machine learning for recognizing the intensity of human engagement intention.

Scientific reports
To interact with humans more precisely and naturally, social robots need to "perceive" human engagement intention, especially need to recognize the main interaction person in multi-person interaction scenarios. By analyzing the intensity of human eng...

How scan parameter choice affects deep learning-based coronary artery disease assessment from computed tomography.

Scientific reports
Recently, algorithms capable of assessing the severity of Coronary Artery Disease (CAD) in form of the Coronary Artery Disease-Reporting and Data System (CAD-RADS) grade from Coronary Computed Tomography Angiography (CCTA) scans using Deep Learning (...

Detection of acute promyelocytic leukemia in peripheral blood and bone marrow with annotation-free deep learning.

Scientific reports
While optical microscopy inspection of blood films and bone marrow aspirates by a hematologist is a crucial step in establishing diagnosis of acute leukemia, especially in low-resource settings where other diagnostic modalities are not available, the...

The Clinical Suitability of an Artificial Intelligence-Enabled Pain Assessment Tool for Use in Infants: Feasibility and Usability Evaluation Study.

Journal of medical Internet research
BACKGROUND: Infants are unable to self-report their pain, which, therefore, often goes underrecognized and undertreated. Adequate assessment of pain, including procedural pain, which has short- and long-term consequences, is critical for its manageme...

Deep-Learning Model for Influenza Prediction From Multisource Heterogeneous Data in a Megacity: Model Development and Evaluation.

Journal of medical Internet research
BACKGROUND: In megacities, there is an urgent need to establish more sensitive forecasting and early warning methods for acute respiratory infectious diseases. Existing prediction and early warning models for influenza and other acute respiratory inf...

Optimizing non-pharmaceutical intervention strategies against COVID-19 using artificial intelligence.

Frontiers in public health
One key task in the early fight against the COVID-19 pandemic was to plan non-pharmaceutical interventions to reduce the spread of the infection while limiting the burden on the society and economy. With more data on the pandemic being generated, it ...