Deep learning enabled brain shunt valve identification using mobile phones

Sujit, Sheeba J.; Bonfante, Eliana; Aein, Azin; Coronado, Ivan; Riascos, Roy; Giancardo, Luca

doi:10.1016/j.cmpb.2021.106356

Cited by 4 publications

(5 citation statements)

References 13 publications

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“…The results indicated that a larger kernel of AlexNet (7 × 7) performs more efficiently on this task, which was deployed on smartphones for convenient and efficient application. 81,82 An LoD of 10 aM was achieved with the optimized RPA primers, crRNA, and SPM, which was comparable to the detection sensitivity of qPCR. FV3 DNA can be detected by PCR in the liver at 4 days postinfection (dpi) and in most organs at 14 dpi.…”

Section: Discussionmentioning

confidence: 84%

“…To the best of our knowledge, this was the first time that fluorescence images from RPA-CRISPR/Cas12a, captured by SPM, were used to calculate the concentration of pathogenic DNA with deep learning. ,, Three deep learning models based on transfer learning performed well on this task, and classification using fluorescence images did not require tiny features. The results indicated that a larger kernel of AlexNet (7 × 7) performs more efficiently on this task, which was deployed on smartphones for convenient and efficient application. , …”

Section: Discussionmentioning

confidence: 99%

“…The results indicated that a larger kernel of AlexNet (7 × 7) performs more efficiently on this task, which was deployed on smartphones for convenient and efficient application. 81 , 82 …”

Section: Discussionmentioning

confidence: 99%

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Detection of Frog Virus 3 by Integrating RPA-CRISPR/Cas12a-SPM with Deep Learning

Lei,

Lian,

Zhang

et al. 2023

ACS Omega

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A fast, easy-to-implement, highly sensitive, and point-of-care (POC) detection system for frog virus 3 (FV3) is proposed. Combining recombinase polymerase amplification (RPA) and CRISPR/Cas12a, a limit of detection (LoD) of 100 aM (60.2 copies/μL) is achieved by optimizing RPA primers and CRISPR RNAs (crRNAs). For POC detection, smartphone microscopy is implemented, and an LoD of 10 aM is achieved in 40 min. The proposed system detects four positive animal-derived samples with a quantitation cycle (Cq) value of quantitative PCR (qPCR) in the range of 13 to 32. In addition, deep learning models are deployed for binary classification (positive or negative samples) and multiclass classification (different concentrations of FV3 and negative samples), achieving 100 and 98.75% accuracy, respectively. Without temperature regulation and expensive equipment, the proposed RPA-CRISPR/Cas12a combined with smartphone readouts and artificial-intelligence-assisted classification showcases the great potential for FV3 detection, specifically POC detection of DNA virus.

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Section: Discussionmentioning

confidence: 84%

Section: Discussionmentioning

confidence: 99%

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Detection of Frog Virus 3 by Integrating RPA-CRISPR/Cas12a-SPM with Deep Learning

Lei,

Lian,

Zhang

et al. 2023

ACS Omega

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“…In comparison, a previous study using a similar deep learning approach with transfer learning achieved a 96% accuracy in identifying five shunt valve types [ 6 ]. Another study reported a detection accuracy of 95% in identifying three shunt valve types using a model trained on smartphone images of X-rays [ 20 ]. Therefore, our study presents a more extensive and accurate deep learning model, both in terms of identification accuracy and the range of identifiable shunt valve types, surpassing the results of the previous studies.…”

Section: Discussionmentioning

confidence: 99%

Applied deep learning in neurosurgery: identifying cerebrospinal fluid (CSF) shunt systems in hydrocephalus patients

Rhomberg,

Trivik-Barrientos,

Hakim

et al. 2024

Acta Neurochir

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Background Over the recent decades, the number of different manufacturers and models of cerebrospinal fluid shunt valves constantly increased. Proper identification of shunt valves on X-ray images is crucial to neurosurgeons and radiologists to derive further details of a specific shunt valve, such as opening pressure settings and MR scanning conditions. The main aim of this study is to evaluate the feasibility of an AI-assisted shunt valve detection system. Methods The dataset used contains 2070 anonymized images of ten different, commonly used shunt valve types. All images were acquired from skull X-rays or scout CT-images. The images were randomly split into a 80% training and 20% validation set. An implementation in Python with the FastAi library was used to train a convolutional neural network (CNN) using a transfer learning method on a pre-trained model. Results Overall, our model achieved an F1-score of 99% to predict the correct shunt valve model. F1-scores for individual shunt valves ranged from 92% for the Sophysa Sophy Mini SM8 to 100% for several other models. Conclusion This technology has the potential to automatically detect different shunt valve models in a fast and precise way and may facilitate the identification of an unknown shunt valve on X-ray or CT scout images. The deep learning model we developed could be integrated into PACS systems or standalone mobile applications to enhance clinical workflows.

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“…The results indicate that a larger kernel of AlexNet (7×7) is more efficient on this task. In addition, to further optimize the detection system, we can deploy deep learning models on smartphones and get the result more conveniently and efficiently (Alkhulaifi et al 2021; Sujit et al 2021).…”

Section: Discussionmentioning

confidence: 99%

Detection of Frog virus 3 via the system integrating RPA-CRISPR/Cas12a-SPM with deep learning

Lei

Lian

Zhang

et al. 2022

Preprint

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Frog virus 3 (FV3, genera Ranavirus, family Iridoviridae), a double-stranded DNA virus, results in irreparable damage to biodiversity and significant economic losses to aquaculture. Although the existing FV3 detection methods are of high sensitivity and specificity, the complex procedure and requirement of expensive instruments limit their practical implantation. Herein, we develop a fast, easy-to-implement, highly sensitive, and point-of-care (POC) detection system for FV3. Combining recombinase polymerase amplification (RPA) and CRISPR/Cas12a, we achieve a limit of detection (LoD) of 100 aM (60.2 copies/μL) by optimizing RPA primers and CRISPR RNAs (crRNAs). For POC detection, we build a smartphone microscopy (SPM) and achieve an LoD of 10 aM within 40 minutes. Four positive animal-derived samples with a quantitation cycle (Cq) value of quantitative PCR (qPCR) in the range of 13 to 32 are detectable by the proposed system. In addition, we deploy deep learning models for binary classification (positive or negative samples) and multiclass classification (different concentrations of FV3 and negative samples), achieving 100% and 98.75% accuracy, respectively. Without temperature regulation and expensive equipment, RPA-CRISPR/Cas12a combined with a smartphone readout and artificial intelligence (AI) assisted classification shows great potential for FV3 detection. This integrated system holds great promise for POC detection of aquatic DNA pathogens.

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Deep learning enabled brain shunt valve identification using mobile phones

Cited by 4 publications

References 13 publications

Detection of Frog Virus 3 by Integrating RPA-CRISPR/Cas12a-SPM with Deep Learning

Detection of Frog Virus 3 by Integrating RPA-CRISPR/Cas12a-SPM with Deep Learning

Applied deep learning in neurosurgery: identifying cerebrospinal fluid (CSF) shunt systems in hydrocephalus patients

Detection of Frog virus 3 via the system integrating RPA-CRISPR/Cas12a-SPM with deep learning

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