AIMC Topic: Reproducibility of Results

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Discovering the hidden messages within cell trajectories using a deep learning approach for in vitro evaluation of cancer drug treatments.

Scientific reports
We describe a novel method to achieve a universal, massive, and fully automated analysis of cell motility behaviours, starting from time-lapse microscopy images. The approach was inspired by the recent successes in application of machine learning for...

Deep Learning Hybrid Method to Automatically Diagnose Periodontal Bone Loss and Stage Periodontitis.

Scientific reports
We developed an automatic method for staging periodontitis on dental panoramic radiographs using the deep learning hybrid method. A novel hybrid framework was proposed to automatically detect and classify the periodontal bone loss of each individual ...

Using artificial neural networks to predict impingement and dislocation in total hip arthroplasty.

Computer methods in biomechanics and biomedical engineering
Dislocation after total hip arthroplasty (THA) remains a major issue and an important post-surgical complication. Impingement and subsequent dislocation are influenced by the design (head size) and position (anteversion and abduction angles) of the a...

Estimating the deep replicability of scientific findings using human and artificial intelligence.

Proceedings of the National Academy of Sciences of the United States of America
Replicability tests of scientific papers show that the majority of papers fail replication. Moreover, failed papers circulate through the literature as quickly as replicating papers. This dynamic weakens the literature, raises research costs, and dem...

The reliability of a deep learning model in clinical out-of-distribution MRI data: A multicohort study.

Medical image analysis
Deep learning (DL) methods have in recent years yielded impressive results in medical imaging, with the potential to function as clinical aid to radiologists. However, DL models in medical imaging are often trained on public research cohorts with ima...

Segmenting nailfold capillaries using an improved U-net network.

Microvascular research
To assess the microcirculation in a patient's capillaries, clinicians often use the valuable and non-invasive diagnostic tool of nailfold capillaroscopy (NC). In particular, evaluating the images that result from NC is particularly important for diag...

Library of deep-learning image segmentation and outcomes model-implementations.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
An open-source library of implementations for deep-learning-based image segmentation and outcomes models based on radiotherapy and radiomics is presented. As oncology treatment planning becomes increasingly driven by automation, such a library of mod...

Deep learning-based monocular placental pose estimation: towards collaborative robotics in fetoscopy.

International journal of computer assisted radiology and surgery
PURPOSE: Twin-to-twin transfusion syndrome (TTTS) is a placental defect occurring in monochorionic twin pregnancies. It is associated with high risks of fetal loss and perinatal death. Fetoscopic elective laser ablation (ELA) of placental anastomoses...