AIMC Topic: Workflow
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Pros and cons of artificial intelligence implementation in diagnostic pathology.
Histopathology
Mar 3, 2024
The rapid introduction of digital pathology has greatly facilitated development of artificial intelligence (AI) models in pathology that have shown great promise in assisting morphological diagnostics and quantitation of therapeutic targets. We are n...
On the pitfalls of Batch Normalization for end-to-end video learning: A study on surgical workflow analysis.
Medical image analysis
Mar 1, 2024
Batch Normalization's (BN) unique property of depending on other samples in a batch is known to cause problems in several tasks, including sequence modeling. Yet, BN-related issues are hardly studied for long video understanding, despite the ubiquito...
Advancing Artificial Intelligence Integration Into the Pathology Workflow: Exploring Opportunities in Gastrointestinal Tract Biopsies.
Laboratory investigation; a journal of technical methods and pathology
Feb 29, 2024
This review aims to present a comprehensive overview of the current landscape of artificial intelligence (AI) applications in the analysis of tubular gastrointestinal biopsies. These publications cover a spectrum of conditions, ranging from inflammat...
End-to-end multimodal 3D imaging and machine learning workflow for non-destructive phenotyping of grapevine trunk internal structure.
Scientific reports
Feb 29, 2024
Quantifying healthy and degraded inner tissues in plants is of great interest in agronomy, for example, to assess plant health and quality and monitor physiological traits or diseases. However, detecting functional and degraded plant tissues in-vivo ...
Deep learning-based workflow for automatic extraction of atria and epicardial adipose tissue on cardiac computed tomography in atrial fibrillation.
Journal of the Chinese Medical Association : JCMA
Feb 21, 2024
BACKGROUND: Preoperative estimation of the volume of the left atrium (LA) and epicardial adipose tissue (EAT) on computed tomography (CT) images is associated with an increased risk of atrial fibrillation (AF) recurrence. We aimed to design a deep le...
Deep learning segmentation of organs-at-risk with integration into clinical workflow for pediatric brain radiotherapy.
Journal of applied clinical medical physics
Feb 19, 2024
PURPOSE: Radiation therapy (RT) of pediatric brain cancer is known to be associated with long-term neurocognitive deficits. Although target and organs-at-risk (OARs) are contoured as part of treatment planning, other structures linked to cognitive fu...
Visualizing Clinical Data Retrieval and Curation in Multimodal Healthcare AI Research: A Technical Note on RIL-workflow.
Journal of imaging informatics in medicine
Feb 16, 2024
Curating and integrating data from sources are bottlenecks to procuring robust training datasets for artificial intelligence (AI) models in healthcare. While numerous applications can process discrete types of clinical data, it is still time-consumin...
Automatic Prediction of Band Gaps of Inorganic Materials Using a Gradient Boosted and Statistical Feature Selection Workflow.
Journal of chemical information and modeling
Feb 6, 2024
Machine learning (ML) methods can train a model to predict material properties by exploiting patterns in materials databases that arise from structure-property relationships. However, the importance of ML-based feature analysis and selection is often...
Deep learning and atlas-based models to streamline the segmentation workflow of total marrow and lymphoid irradiation.
La Radiologia medica
Feb 2, 2024
PURPOSE: To improve the workflow of total marrow and lymphoid irradiation (TMLI) by enhancing the delineation of organs at risk (OARs) and clinical target volume (CTV) using deep learning (DL) and atlas-based (AB) segmentation models.
Radiologists' perspectives on the workflow integration of an artificial intelligence-based computer-aided detection system: A qualitative study.
Applied ergonomics
Feb 1, 2024
In healthcare, artificial intelligence (AI) is expected to improve work processes, yet most research focuses on the technical features of AI rather than its real-world clinical implementation. To evaluate the implementation process of an AI-based com...
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