AIMC Topic: Deep Learning

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Deep learning-based automated measurement of hip key angles and auxiliary diagnosis of developmental dysplasia of the hip.

BMC musculoskeletal disorders
OBJECTIVES: Anteroposterior pelvic radiographs remains the most widely employed method for diagnosing developmental dysplasia of the hip. This study aims to evaluate the accuracy of an artificial intelligence model in measuring angles in pelvic radio...

A two-stage deep-learning model for determination of the contact of mandibular third molars with the mandibular canal on panoramic radiographs.

BMC oral health
OBJECTIVES: This study aimed to assess the accuracy of a two-stage deep learning (DL) model for (1) detecting mandibular third molars (MTMs) and the mandibular canal (MC), and (2) classifying the anatomical relationship between these structures (cont...

Automatic face detection based on bidirectional recurrent neural network optimized by improved Ebola optimization search algorithm.

Scientific reports
Face detection is a multidisciplinary research subject that employs fundamental computer algorithms, image processing, and patterning. Neural networks, on the other hand, have been widely developed to solve challenges in the domains of feature extrac...

Deep learning enables the use of ultra-high-density array in DNBSEQ.

Scientific reports
DNBSEQ employs a patterned array to facilitate massively parallel sequencing of DNA nanoballs (DNBs), leading to a considerable boost in throughput. By employing the ultra-high-density (UHD) array with an increased density of DNB binding sites, the t...

Brain tumor diagnosis in MRI scans images using Residual/Shuffle Network optimized by augmented Falcon Finch optimization.

Scientific reports
Brain tumor diagnosis is an important task in prognosing and treatment planning of the patients with brain cancer. in the meantime, using the Magnetic Resonance Imaging (MRI) as a commonly used non-invasive imaging technique provide the experts a hel...

Comprehensive Symptom Prediction in Inpatients With Acute Psychiatric Disorders Using Wearable-Based Deep Learning Models: Development and Validation Study.

Journal of medical Internet research
BACKGROUND: Assessing the complex and multifaceted symptoms of patients with acute psychiatric disorders proves to be significantly challenging for clinicians. Moreover, the staff in acute psychiatric wards face high work intensity and risk of burnou...

Task-agnostic exoskeleton control via biological joint moment estimation.

Nature
Lower-limb exoskeletons have the potential to transform the way we move, but current state-of-the-art controllers cannot accommodate the rich set of possible human behaviours that range from cyclic and predictable to transitory and unstructured. We i...

Elevating Patient Care With Deep Learning: High-Resolution Cervical Auscultation Signals for Swallowing Kinematic Analysis in Nasogastric Tube Patients.

IEEE journal of translational engineering in health and medicine
Patients with nasogastric (NG) tubes require careful monitoring due to the potential impact of the tube on their ability to swallow safely. This study aimed to investigate the utility of high-resolution cervical auscultation (HRCA) signals in assessi...

Harnessing hybrid deep learning approach for personalized retrieval in e-learning.

PloS one
The current worldwide pandemic has significantly increased the need for online learning platforms, hence presenting difficulty in choosing appropriate course materials from the vast online educational resources due to user knowledge frameworks variat...

Drug Sensitivity Prediction Based on Multi-stage Multi-modal Drug Representation Learning.

Interdisciplinary sciences, computational life sciences
Accurate prediction of anticancer drug responses is essential for developing personalized treatment plans in order to improve cancer patient survival rates and reduce healthcare costs. To this end, we propose a drug sensitivity prediction model based...