AIMC Topic: Reproducibility of Results

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Artificial intelligence in coronary artery calcium score: rationale, different approaches, and outcomes.

The international journal of cardiovascular imaging
Almost 35 years after its introduction, coronary artery calcium score (CACS) not only survived technological advances but became one of the cornerstones of contemporary cardiovascular imaging. Its simplicity and quantitative nature established it as ...

Artificial intelligence in colonoscopy: from detection to diagnosis.

The Korean journal of internal medicine
This study reviews the recent progress of artificial intelligence for colonoscopy from detection to diagnosis. The source of data was 27 original studies in PubMed. The search terms were "colonoscopy" (title) and "deep learning" (abstract). The eligi...

Machine Teaching Allows for Rapid Development of Automated Systems for Retinal Lesion Detection From Small Image Datasets.

Ophthalmic surgery, lasers & imaging retina
Machine teaching, a machine learning subfield, may allow for rapid development of artificial intelligence systems able to automatically identify emerging ocular biomarkers from small imaging datasets. We sought to use machine teaching to automaticall...

An Artificial Intelligence-Driven Approach for Automatic Evaluation of Right-to-Left Shunt Grades in Saline-Contrasted Transthoracic Echocardiography.

Ultrasound in medicine & biology
BACKGROUND: Intracardiac or pulmonary right-to-left shunt (RLS) is a common cardiac anomaly associated with an increased risk of neurological disorders, specifically cryptogenic stroke. Saline-contrasted transthoracic echocardiography (scTTE) is ofte...

REFORMS: Consensus-based Recommendations for Machine-learning-based Science.

Science advances
Machine learning (ML) methods are proliferating in scientific research. However, the adoption of these methods has been accompanied by failures of validity, reproducibility, and generalizability. These failures can hinder scientific progress, lead to...

Impact of F-FDG PET Intensity Normalization on Radiomic Features of Oropharyngeal Squamous Cell Carcinomas and Machine Learning-Generated Biomarkers.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
We aimed to investigate the effects of F-FDG PET voxel intensity normalization on radiomic features of oropharyngeal squamous cell carcinoma (OPSCC) and machine learning-generated radiomic biomarkers. We extracted 1,037 F-FDG PET radiomic features q...

Establishment and validation of an interactive artificial intelligence platform to predict postoperative ambulatory status for patients with metastatic spinal disease: a multicenter analysis.

International journal of surgery (London, England)
BACKGROUND: Identification of patients with high-risk of experiencing inability to walk after surgery is important for surgeons to make therapeutic strategies for patients with metastatic spinal disease. However, there is a lack of clinical tool to a...

BlazePose-Seq2Seq: Leveraging Regular RGB Cameras for Robust Gait Assessment.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Evaluation of human gait through smartphone-based pose estimation algorithms provides an attractive alternative to costly lab-bound instrumented assessment and offers a paradigm shift with real time gait capture for clinical assessment. Systems based...

Developing and validating a clinlabomics-based machine-learning model for early detection of retinal detachment in patients with high myopia.

Journal of translational medicine
BACKGROUND: Retinal detachment (RD) is a vision-threatening disorder of significant severity. Individuals with high myopia (HM) face a 2 to 6 times higher risk of developing RD compared to non-myopes. The timely identification of high myopia-related ...

Assessing the reliability of point mutation as data augmentation for deep learning with genomic data.

BMC bioinformatics
BACKGROUND: Deep neural networks (DNNs) have the potential to revolutionize our understanding and treatment of genetic diseases. An inherent limitation of deep neural networks, however, is their high demand for data during training. To overcome this ...