AIMC Topic: Algorithms

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A novel survival prediction signature outperforms PAM50 and artificial intelligence-based feature-selection methods.

Computational biology and chemistry
The robustness of a breast cancer gene signature, the super-proliferation set (SPS), is initially tested and investigated on breast cancer cell lines from the Cancer Cell Line Encyclopaedia (CCLE). Previously, SPS was derived via a meta-analysis of 4...

Automated postural asymmetry assessment in infants neurodevelopmental evaluation using novel video-based features.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Neurodevelopmental assessment enables the identification of infant developmental disorders in the first months of life. Thus, the appropriate therapy can be initiated promptly, increasing the chances for correct motor functi...

Artificial Intelligence for Cardiothoracic Imaging: Overview of Current and Emerging Applications.

Seminars in roentgenology
Artificial intelligence algorithms can learn by assimilating information from large datasets in order to decipher complex associations, identify previously undiscovered pathophysiological states, and construct prediction models. There has been tremen...

Artificial intelligence in clinical multiparameter flow cytometry and mass cytometry-key tools and progress.

Seminars in diagnostic pathology
There are many research studies and emerging tools using artificial intelligence (AI) and machine learning to augment flow and mass cytometry workflows. Emerging AI tools can quickly identify common cell populations with continuous improvement of acc...

Machine learning to improve false-positive results in the Dutch newborn screening for congenital hypothyroidism.

Clinical biochemistry
OBJECTIVE: The Dutch Congenital hypothyroidism (CH) Newborn Screening (NBS) algorithm for thyroidal and central congenital hypothyroidism (CH-T and CH-C, respectively) is primarily based on determination of thyroxine (T4) concentrations in dried bloo...

Comparison of Deep-Learning Image Reconstruction With Hybrid Iterative Reconstruction for Evaluating Lung Nodules With High-Resolution Computed Tomography.

Journal of computer assisted tomography
OBJECTIVE: This study aimed to investigate the impact of deep-learning reconstruction (DLR) on the detailed evaluation of solitary lung nodule using high-resolution computed tomography (HRCT) compared with hybrid iterative reconstruction (hybrid IR).

TrDosePred: A deep learning dose prediction algorithm based on transformers for head and neck cancer radiotherapy.

Journal of applied clinical medical physics
BACKGROUND: Intensity-Modulated Radiation Therapy (IMRT) has been the standard of care for many types of tumors. However, treatment planning for IMRT is a time-consuming and labor-intensive process.

[Possible Radiation Dose Reduction in Abdominal Plain CT Using Deep Learning Reconstruction].

Nihon Hoshasen Gijutsu Gakkai zasshi
PURPOSE: The purposes of this study were to evaluate the low-contrast detectability of CT images assuming hepatocellular carcinoma and to determine whether dose reduction in abdominal plain CT imaging is possible.

NLS: An accurate and yet easy-to-interpret prediction method.

Neural networks : the official journal of the International Neural Network Society
Over the last years, the predictive power of supervised machine learning (ML) has undergone impressive advances, achieving the status of state of the art and super-human level in some applications. However, the employment rate of ML models in real-li...

An Artificial Plant Community Algorithm for the Accurate Range-Free Positioning of Wireless Sensor Networks.

Sensors (Basel, Switzerland)
The problem of positioning wireless sensor networks is an important and challenging topic in all walks of life. Inspired by the evolution behavior of natural plant communities and traditional positioning algorithms, a novel positioning algorithm base...