scholarly journals Deep Learning Analysis of in Vivo Hyperspectral Images for Automated Intraoperative Nerves Detection

2021 ◽  
Author(s):  
Manuel Barberio ◽  
Toby Collins ◽  
Valentin Bencteux ◽  
Richard Nkusi ◽  
Eric Felli ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cross Validation ◽  
State Of The Art ◽  
Invasive Technique ◽  
Midline Incision ◽  
Average Sensitivity ◽  
Non Invasive ◽  
First Time ◽  
Learning Analysis

Abstract Nerves are difficult to recognize during surgery and inadvertent injuries may occur, bringing catastrophic consequences for the patient. Hyperspectral imaging (HSI) is a non-invasive technique combining photography with spectroscopy, allowing biological tissue property quantification. We show for the first time that HSI combined with deep learning allows nerves and other tissue types to be automatically recognized in-vivo at the pixel level. An animal model is used comprising eight anesthetized pigs with a neck midline incision, exposing several structures (nerve, artery, vein, muscle, fat, skin). State-of-the-art machine learning models have been trained to recognize these tissue types in HSI data. The best model is a Convolutional Neural Network (CNN), achieving an overall average sensitivity of 0.91 and specificity of 0.99, validated with leave-one-patient-out cross-validation. For the nerve, the CNN achieves an average sensitivity of 0.76 and specificity of 1.0. In conclusion, HSI combined with a CNN model is suitable for in vivo nerve recognition.

scholarly journals Deep Learning Analysis of In Vivo Hyperspectral Images for Automated Intraoperative Nerve Detection

Diagnostics ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1508
Author(s):  
Manuel Barberio ◽  
Toby Collins ◽  
Valentin Bencteux ◽  
Richard Nkusi ◽  
Eric Felli ◽  
...  
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Hyperspectral Images ◽  
Invasive Technique ◽  
Average Sensitivity ◽  
Non Invasive ◽  
First Time ◽  
Learning Analysis

Nerves are critical structures that may be difficult to recognize during surgery. Inadvertent nerve injuries can have catastrophic consequences for the patient and lead to life-long pain and a reduced quality of life. Hyperspectral imaging (HSI) is a non-invasive technique combining photography with spectroscopy, allowing non-invasive intraoperative biological tissue property quantification. We show, for the first time, that HSI combined with deep learning allows nerves and other tissue types to be automatically recognized in in vivo hyperspectral images. An animal model was used, and eight anesthetized pigs underwent neck midline incisions, exposing several structures (nerve, artery, vein, muscle, fat, skin). State-of-the-art machine learning models were trained to recognize these tissue types in HSI data. The best model was a convolutional neural network (CNN), achieving an overall average sensitivity of 0.91 and a specificity of 1.0, validated with leave-one-patient-out cross-validation. For the nerve, the CNN achieved an average sensitivity of 0.76 and a specificity of 0.99. In conclusion, HSI combined with a CNN model is suitable for in vivo nerve recognition.

scholarly journals Electro-Quasistatic Animal Body Communication for Untethered Rodent Biopotential Recording

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shreeya Sriram ◽  
Shitij Avlani ◽  
Matthew P. Ward ◽  
Shreyas Sen
Keyword(s):  
Lower Energy ◽  
Sensor Node ◽  
State Of The Art ◽  
The Body ◽  
Animal Body ◽  
Current State ◽  
Communication Modalities ◽  
First Time ◽  
Biopotential Recording

AbstractContinuous multi-channel monitoring of biopotential signals is vital in understanding the body as a whole, facilitating accurate models and predictions in neural research. The current state of the art in wireless technologies for untethered biopotential recordings rely on radiative electromagnetic (EM) fields. In such transmissions, only a small fraction of this energy is received since the EM fields are widely radiated resulting in lossy inefficient systems. Using the body as a communication medium (similar to a ’wire’) allows for the containment of the energy within the body, yielding order(s) of magnitude lower energy than radiative EM communication. In this work, we introduce Animal Body Communication (ABC), which utilizes the concept of using the body as a medium into the domain of untethered animal biopotential recording. This work, for the first time, develops the theory and models for animal body communication circuitry and channel loss. Using this theoretical model, a sub-inch$$^3$$ 3 [1″ × 1″ × 0.4″], custom-designed sensor node is built using off the shelf components which is capable of sensing and transmitting biopotential signals, through the body of the rat at significantly lower powers compared to traditional wireless transmissions. In-vivo experimental analysis proves that ABC successfully transmits acquired electrocardiogram (EKG) signals through the body with correlation $$>99\%$$ > 99 % when compared to traditional wireless communication modalities, with a 50$$\times$$ × reduction in power consumption.

scholarly journals Optical coherence tomography imaging of oral mucosa bullous diseases: a preliminary study

2020 ◽  
Vol 49 (2) ◽  
pp. 20190071
Author(s):  
Dario Di Stasio ◽  
Dorina Lauritano ◽  
Francesca Loffredo ◽  
Enrica Gentile ◽  
Fedora Della Vella ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Oral Mucosa ◽  
Statistical Power ◽  
Pemphigus Vulgaris ◽  
Invasive Technique ◽  
Optical Coherence ◽  
Non Invasive ◽  
Bullous Diseases ◽  
Subepithelial Layer

Objectives: Optical coherence tomography (OCT) is a non-invasive technique based on optical imaging with a micrometre resolution. The purpose of this study is to investigate the potential role of OCT in evaluating oral mucosa bullous diseases. Methods: two patients with bullous pemphigoid (BP) and one patient with pemphigus vulgaris (PV) were examined and images of their oral lesions were performed using OCT. Results: In OCT images, the BP blister has a clearly different morphology from the PV one compared to the blistering level. Conclusion: This exploratory study suggests that the OCT is able to distinguish epithelial and subepithelial layer in vivo images of healthy oral mucosa from those with bullous diseases, assisting the clinicians in differential diagnosis.The presented data are in accordance with the scientific literature, although a wider pool of cases is needed to increase statistical power. Histological examination and immunofluorescence methods remain the gold standard for the diagnosis of oral bullous diseases. In this context, the OCT can provide the clinician with a valuable aid both as an additional diagnostic tool and in the follow up of the disease.

scholarly journals Confocal Raman spectroscopy: Evaluation of a non-invasive technique for the detection of topically applied ketorolac tromethamine in vitro and in vivo

2019 ◽  
Vol 570 ◽  
pp. 118641 ◽  
Author(s):  
Christian J.F. Bertens ◽  
Shuo Zhang ◽  
Roel J. Erckens ◽  
Frank J.H.M. van den Biggelaar ◽  
Tos T.J.M. Berendschot ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Invasive Technique ◽  
Ketorolac Tromethamine ◽  
Confocal Raman Spectroscopy ◽  
Non Invasive ◽  
Confocal Raman

scholarly journals Exploitation of deep learning in the automatic detection of cracks on paved roads

GEOMATICA ◽  
2019 ◽  
Vol 73 (2) ◽  
pp. 29-44
Author(s):  
Won Mo Jung ◽  
Faizaan Naveed ◽  
Baoxin Hu ◽  
Jianguo Wang ◽  
Ningyuan Li
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Ground Truth ◽  
Learning Networks ◽  
Test Image ◽  
Convolutional Network ◽  
Image Patches ◽  
Severity Levels ◽  
First Time ◽  
Different Levels

With the advance of deep learning networks, their applications in the assessment of pavement conditions are gaining more attention. A convolutional neural network (CNN) is the most commonly used network in image classification. In terms of pavement assessment, most existing CNNs are designed to only distinguish between cracks and non-cracks. Few networks classify cracks in different levels of severity. Information on the severity of pavement cracks is critical for pavement repair services. In this study, the state-of-the-art CNN used in the detection of pavement cracks was improved to localize the cracks and identify their distress levels based on three categories (low, medium, and high). In addition, a fully convolutional network (FCN) was, for the first time, utilized in the detection of pavement cracks. These designed architectures were validated using the data acquired on four highways in Ontario, Canada, and compared with the ground truth that was provided by the Ministry of Transportation of Ontario (MTO). The results showed that with the improved CNN, the prediction precision on a series of test image patches were 72.9%, 73.9%, and 73.1% for cracks with the severity levels of low, medium, and high, respectively. The precision for the FCN was tested on whole pavement images, resulting in 62.8%, 63.3%, and 66.4%, respectively, for cracks with the severity levels of low, medium, and high. It is worth mentioning that the ground truth contained some uncertainties, which partially contributed to the relatively low precision.

scholarly journals Can Autism Be Diagnosed with Artificial Intelligence? A Narrative Review

Diagnostics ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 2032
Author(s):  
Ahmad Chaddad ◽  
Jiali Li ◽  
Qizong Lu ◽  
Yujie Li ◽  
Idowu Paul Okuwobi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Texture Analysis ◽  
Molecular Data ◽  
Autism Spectrum ◽  
Invasive Technique ◽  
Non Invasive ◽  
Learning Techniques ◽  
Healthy Control ◽  
Aided Diagnosis

Radiomics with deep learning models have become popular in computer-aided diagnosis and have outperformed human experts on many clinical tasks. Specifically, radiomic models based on artificial intelligence (AI) are using medical data (i.e., images, molecular data, clinical variables, etc.) for predicting clinical tasks such as autism spectrum disorder (ASD). In this review, we summarized and discussed the radiomic techniques used for ASD analysis. Currently, the limited radiomic work of ASD is related to the variation of morphological features of brain thickness that is different from texture analysis. These techniques are based on imaging shape features that can be used with predictive models for predicting ASD. This review explores the progress of ASD-based radiomics with a brief description of ASD and the current non-invasive technique used to classify between ASD and healthy control (HC) subjects. With AI, new radiomic models using the deep learning techniques will be also described. To consider the texture analysis with deep CNNs, more investigations are suggested to be integrated with additional validation steps on various MRI sites.

scholarly journals In vivo contactless brain stimulation via non-invasive and targeted delivery of magnetoelectric nanoparticles

2020 ◽  
Author(s):  
Tyler Nguyen ◽  
Jianhua Gao ◽  
Ping Wang ◽  
Abhignyan Nagesetti ◽  
Peter Andrews ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Brain Stimulation ◽  
Targeted Delivery ◽  
Brain Region ◽  
Magnetic Field Gradient ◽  
Latency Period ◽  
Global Network ◽  
Invasive Technique ◽  
Non Invasive

AbstractNon-invasive brain stimulation is valuable for studying neural circuits and treating various neurological disorders in humans. However, the current technologies usually have low spatial and temporal precision and poor brain penetration, which greatly limit their application. A new class of nanoparticles known as magneto-electric nanoparticles (MENs) can be navigated to a targeted brain region with a magnetic field and is highly efficient in converting an externally applied magnetic wave into local electric fields for neuronal activity modulation. Here we developed a new method to fabricate MENs of CoFe2O4-BaTiO3 core-shell structure that had excellent magneto-electrical coupling properties. Using calcium imaging of organotypic and acute cortical slices from GCaMP6s transgenic mice, we demonstrated their efficacy in reliably evoking neuronal responses with a short latency period. For in vivo non-invasive delivery of MENs to brain, fluorescently labeled MENs were intravenously injected and guided to pass through the blood brain barrier to a targeted brain region by applying a magnetic field gradient. A magnetic field (∼450 Oe at 10 Hz) applied to mouse brain was able to reliably evoke cortical activities, as revealed by in vivo two-photon and mesoscopic imaging of calcium signals at both cellular and global network levels. The effect was further confirmed by the increased number of c-Fos expressing cells after stimulation. Neither brain delivery of MENs nor the subsequent magnetic stimulation caused any significant increases in the numbers of GFAP and IBA1 positive astrocytes and microglia in the brain. This study demonstrates the feasibility of using MENs as a novel efficient and non-invasive technique of contactless deep brain stimulation that may have great potential for translation.

scholarly journals Deep Learning for Non-Invasive Cortical Potential Imaging

2020 ◽  
Author(s):  
Alexandra Razorenova ◽  
Nikolay Yavich ◽  
Mikhail Malovichko ◽  
Maxim Fedorov ◽  
Nikolay Koshev ◽  
...  
Keyword(s):  
Inverse Problem ◽  
Deep Learning ◽  
Electric Fields ◽  
Spatial Resolution ◽  
Brain Activity ◽  
Invasive Technique ◽  
Problem Solver ◽  
Deep Convolutional Neural Networks ◽  
Non Invasive ◽  
Cortical Potential Imaging

AbstractElectroencephalography (EEG) is a well-established non-invasive technique to measure the brain activity, albeit with a limited spatial resolution. Variations in electric conductivity between different tissues distort the electric fields generated by cortical sources, resulting in smeared potential measurements on the scalp. One needs to solve an ill-posed inverse problem to recover the original neural activity. In this article, we present a generic method of recovering the cortical potentials from the EEG measurement by introducing a new inverse-problem solver based on deep Convolutional Neural Networks (CNN) in paired (U-Net) and unpaired (DualGAN) configurations. The solvers were trained on synthetic EEG-ECoG pairs that were generated using a head conductivity model computed using the Finite Element Method (FEM). These solvers are the first of their kind, that provide robust translation of EEG data to the cortex surface using deep learning. Providing a fast and accurate interpretation of the tracked EEG signal, our approach promises a boost to the spatial resolution of the future EEG devices.

scholarly journals A structure-based deep learning framework for protein engineering

2019 ◽  
Author(s):  
Raghav Shroff ◽  
Austin W. Cole ◽  
Barrett R. Morrow ◽  
Daniel J. Diaz ◽  
Isaac Donnell ◽  
...  
Keyword(s):  
Neural Network ◽  
Amino Acids ◽  
Deep Learning ◽  
Protein Engineering ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
A Priori ◽  
Novel Proteins ◽  
Learning Framework

AbstractWhile deep learning methods exist to guide protein optimization, examples of novel proteins generated with these techniques require a priori mutational data. Here we report a 3D convolutional neural network that associates amino acids with neighboring chemical microenvironments at state-of-the-art accuracy. This algorithm enables identification of novel gain-of-function mutations, and subsequent experiments confirm substantive phenotypic improvements in stability-associated phenotypes in vivo across three diverse proteins.

scholarly journals Non-invasive scoring of cellular atypia in keratinocyte cancers in 3D LC-OCT images using Deep Learning

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Sébastien Fischman ◽  
Javiera Pérez-Anker ◽  
Linda Tognetti ◽  
Angelo Di Naro ◽  
Mariano Suppa ◽  
...  
Keyword(s):  
Deep Learning ◽  
Invasive Procedure ◽  
Skin Lesions ◽  
Small Sample ◽  
Three Dimensions ◽  
Line Field ◽  
Biologically Relevant ◽  
Non Invasive ◽  
Cellular Atypia

AbstractDiagnosis based on histopathology for skin cancer detection is today’s gold standard and relies on the presence or absence of biomarkers and cellular atypia. However it suffers drawbacks: it requires a strong expertise and is time-consuming. Moreover the notion of atypia or dysplasia of the visible cells used for diagnosis is very subjective, with poor inter-rater agreement reported in the literature. Lastly, histology requires a biopsy which is an invasive procedure and only captures a small sample of the lesion, which is insufficient in the context of large fields of cancerization. Here we demonstrate that the notion of cellular atypia can be objectively defined and quantified with a non-invasive in-vivo approach in three dimensions (3D). A Deep Learning (DL) algorithm is trained to segment keratinocyte (KC) nuclei from Line-field Confocal Optical Coherence Tomography (LC-OCT) 3D images. Based on these segmentations, a series of quantitative, reproducible and biologically relevant metrics is derived to describe KC nuclei individually. We show that, using those metrics, simple and more complex definitions of atypia can be derived to discriminate between healthy and pathological skins, achieving Area Under the ROC Curve (AUC) scores superior than 0.965, largely outperforming medical experts on the same task with an AUC of 0.766. All together, our approach and findings open the door to a precise quantitative monitoring of skin lesions and treatments, offering a promising non-invasive tool for clinical studies to demonstrate the effects of a treatment and for clinicians to assess the severity of a lesion and follow the evolution of pre-cancerous lesions over time.

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