AIMC Topic: Deep Learning

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Using deep learning-derived image features in radiologic time series to make personalised predictions: proof of concept in colonic transit data.

European radiology
OBJECTIVES: Siamese neural networks (SNN) were used to classify the presence of radiopaque beads as part of a colonic transit time study (CTS). The SNN output was then used as a feature in a time series model to predict progression through a CTS.

Using Deep Learning to Detect the Presence and Location of Hemoperitoneum on the Focused Assessment with Sonography in Trauma (FAST) Examination in Adults.

Journal of digital imaging
Abdominal ultrasonography has become an integral component of the evaluation of trauma patients. Internal hemorrhage can be rapidly diagnosed by finding free fluid with point-of-care ultrasound (POCUS) and expedite decisions to perform lifesaving int...

A CT-based deep learning system for automatic assessment of aortic root morphology for TAVI planning.

Computers in biology and medicine
Accurate planning of transcatheter aortic valve implantation (TAVI) is important to minimize complications, and it requires anatomic evaluation of the aortic root (AR), commonly performed through 3D computed tomography (CT) image analysis. Currently,...

A deep learning approach for radiological detection and classification of radicular cysts and periapical granulomas.

Journal of dentistry
OBJECTIVES: Dentists and oral surgeons often face difficulties distinguishing between radicular cysts and periapical granulomas on panoramic imaging. Radicular cysts require surgical removal while root canal treatment is the first-line treatment for ...

Probabilistic real-time natural gas jet fire consequence modeling of offshore platforms by hybrid deep learning approach.

Marine pollution bulletin
Natural gas jet fire induced by igniting blowouts has the potential to cause critical structure damage and great casualties of offshore platforms. Real-time natural gas jet fire plume prediction is essential to support the emergency planning to mitig...

Deep learning-based scoring of tumour-infiltrating lymphocytes is prognostic in primary melanoma and predictive to PD-1 checkpoint inhibition in melanoma metastases.

EBioMedicine
BACKGROUND: Recent advances in digital pathology have enabled accurate and standardised enumeration of tumour-infiltrating lymphocytes (TILs). Here, we aim to evaluate TILs as a percentage electronic TIL score (eTILs) and investigate its prognostic a...

Integrating domain knowledge for biomedical text analysis into deep learning: A survey.

Journal of biomedical informatics
The past decade has witnessed an explosion of textual information in the biomedical field. Biomedical texts provide a basis for healthcare delivery, knowledge discovery, and decision-making. Over the same period, deep learning has achieved remarkable...

Incremental retraining, clinical implementation, and acceptance rate of deep learning auto-segmentation for male pelvis in a multiuser environment.

Medical physics
BACKGROUND: Deep learning auto-segmentation (DLAS) models have been adopted in the clinic; however, they suffer from performance deterioration owing to the clinical practice variability. Some commercial DLAS software provide an incremental retraining...