AIMC Topic: Deep Learning

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Cell-free biosynthesis combined with deep learning accelerates de novo-development of antimicrobial peptides.

Nature communications
Bioactive peptides are key molecules in health and medicine. Deep learning holds a big promise for the discovery and design of bioactive peptides. Yet, suitable experimental approaches are required to validate candidates in high throughput and at low...

Dental Caries Detection and Classification in CBCT Images Using Deep Learning.

International dental journal
OBJECTIVES: This study aimed to investigate the accuracy of deep learning algorithms to diagnose tooth caries and classify the extension and location of dental caries in cone beam computed tomography (CBCT) images. To the best of our knowledge, this ...

High-Content Image-Based Screening and Deep Learning for the Detection of Anti-Inflammatory Drug Leads.

Chembiochem : a European journal of chemical biology
We developed a high-content image-based screen that utilizes the pro-inflammatory stimulus lipopolysaccharide (LPS) and murine macrophages (RAW264.7) with the goal of enabling the identification of novel anti-inflammatory lead compounds. We screened ...

Deep Learning Based Prediction of Myopia Control Effect in Children Treated With Overnight Orthokeratology.

Eye & contact lens
OBJECTIVES: To develop and validate a deep learning-based model for predicting 12-month axial length (AL) elongation using baseline factors and early corneal topographic changes in children treated with orthokeratology (Ortho-K) and to investigate th...

Automatic Detection and Classification of Modic Changes in MRI Images Using Deep Learning: Intelligent Assisted Diagnosis System.

Orthopaedic surgery
OBJECTIVE: Modic changes (MCs) are the most prevalent classification system for describing intravertebral MRI signal intensity changes. However, interpreting these intricate MRI images is a complex and time-consuming process. This study investigates ...

Differentiating adrenal metastases from benign lesions with multiphase CT imaging: Deep learning could play an active role in assisting radiologists.

European journal of radiology
OBJECTIVES: To develop and externally validate multiphase CT-based deep learning (DL) models for differentiating adrenal metastases from benign lesions.

Classification of helical polymers with deep-learning language models.

Journal of structural biology
Many macromolecules in biological systems exist in the form of helical polymers. However, the inherent polymorphism and heterogeneity of samples complicate the reconstruction of helical polymers from cryo-EM images. Currently, available 2D classifica...

Deep learning, 3D ultrastructural analysis reveals quantitative differences in platelet and organelle packing in COVID-19/SARSCoV2 patient-derived platelets.

Platelets
Platelets contribute to COVID-19 clinical manifestations, of which microclotting in the pulmonary vasculature has been a prominent symptom. To investigate the potential diagnostic contributions of overall platelet morphology and their α-granules and ...

A multi-task deep learning model for EGFR genotyping prediction and GTV segmentation of brain metastasis.

Journal of translational medicine
BACKGROUND: The precise prediction of epidermal growth factor receptor (EGFR) mutation status and gross tumor volume (GTV) segmentation are crucial goals in computer-aided lung adenocarcinoma brain metastasis diagnosis. However, these two tasks prese...