AIMC Topic: Algorithms

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Technical and regulatory challenges in artificial intelligence-based pulse oximetry: a proposed development pipeline.

British journal of anaesthesia
Pulse oximetry, although generally effective under ideal conditions, faces challenges in accurately estimating peripheral oxygen saturation (SpO) in complex clinical scenarios, particularly at lower saturation levels and in patients with darker skin ...

Enhanced dose prediction for head and neck cancer artificial intelligence-driven radiotherapy based on transfer learning with limited training data.

Journal of applied clinical medical physics
PURPOSE: Training deep learning dose prediction models for the latest cutting-edge radiotherapy techniques, such as AI-based nodal radiotherapy (AINRT) and Daily Adaptive AI-based nodal radiotherapy (DA-AINRT), is challenging due to limited data. Thi...

TEDML: a new machine learning (ML) approach for predicting thyroid eye disease and identifying key biomarkers.

The Journal of endocrinology
Thyroid eye disease (TED) features immune infiltration and metabolic dysregulation. Understanding these processes and identifying potential biomarkers are crucial for improving diagnosis and treatment. To this end, immune cell infiltration was analyz...

Employing Automated Machine Learning (AutoML) Methods to Facilitate the ADMET Properties Prediction.

Journal of chemical information and modeling
The rationale for using ADMET prediction tools in the early drug discovery paradigm is to guide the design of new compounds with favorable ADMET properties and ultimately minimize the attrition rates of drug failures. Artificial intelligence (AI) in ...

Unsupervised learning to identify symptom clusters in older adults undergoing chemotherapy.

Journal of geriatric oncology
INTRODUCTION: Unsupervised machine learning (ML) approaches such as clustering have not been commonly applied to patient-reported data. This study describes ML methods to explore and describe patient-reported symptom trajectories in older adults rece...

Semi-Automated Multi-Label Classification of Autistic Mannerisms by Machine Learning on Post Hoc Skeletal Tracking.

Autism research : official journal of the International Society for Autism Research
Mannerisms describe repetitive or unconventional body movements like arm flapping. These movements are early markers of restricted and repetitive behaviors (RRBs) in autism spectrum disorder (ASD). However, assessing mannerisms reliably is challengin...

Adaptive Vectorial Restoration from Dynamic Speckle Patterns Through Biological Scattering Media Based on Deep Learning.

Sensors (Basel, Switzerland)
Imaging technologies based on vector optical fields hold significant potential in the biomedical field, particularly for non-invasive scattering imaging of anisotropic biological tissues. However, the dynamic and anisotropic nature of biological tiss...

Performance and limitation of machine learning algorithms for diabetic retinopathy screening and its application in health management: a meta-analysis.

Biomedical engineering online
BACKGROUND: In recent years, artificial intelligence and machine learning algorithms have been used more extensively to diagnose diabetic retinopathy and other diseases. Still, the effectiveness of these methods has not been thoroughly investigated. ...

Analysis of the factors influencing the salary level and satisfaction of medical staff in hospitals in less developed areas of Western China based on machine learning algorithms: evidence from Guangxi Zhuang Autonomous Region.

BMC health services research
BACKGROUND: Compensation plays a critical role in motivating staff and enhancing operational performance and human resource costs in hospitals. This study was aimed at investigating pay levels and the key factors influencing pay satisfaction in secon...

Structured hashing with deep learning for modality, organ, and disease content sensitive medical image retrieval.

Scientific reports
Evidence-based medicine is the preferred procedure among clinicians for treating patients. Content-based medical image retrieval (CBMIR) is widely used to extract evidence from a large archive of medical images. Developing effective CBMIR systems for...