Automated label‐free detection of injured neuron with deep learning by two‐photon microscopy

Wang, Shu; Lin, Bin; Lin, Guimin; Lin, Ruolan; Huang, Feng; Li, Weilin; Wang, Xingfu; Liu, Xueyong; Zhang, Yu; Wang, Feng; Lin, Yuanxiang; Chen, Lidian; Chen, Jianxin

doi:10.1002/jbio.201960062

Cited by 14 publications

(11 citation statements)

References 40 publications

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“…273 Wang et al used a CNN model to automatically detect focal cerebral ischemia-reperfusion-injured neurons in label-free two-photon microscopic (TPM) images. 274 This model significantly improves diagnostic accuracy compared with standard histology and detects the location of the injured neuron without prior knowledge of histopathology. 274 The study used 64 × 64 pixels input images to be fed into a Residual Network CNN (ResNet CNN), which reduces the spatial resolution of the input.…”

Section: Aaementioning

confidence: 99%

“…274 Though this study is not directly related to druginduced toxicity, the approach could be extrapolated to neurotoxicological analysis to yield robust toxicity quantification tools that in turn shall reduce the need of using more laboratory animals to get robust results. 273,274 Drug-induced toxicity is tightly related to hepatic and cardiac adverse effects of drugs, and more than 75% of postmarketing withdrawals of drugs are due to these two causes. 275−277 These toxicities are intricately related to the disruption of the subcellular structures.…”

Section: Aaementioning

confidence: 99%

“…With the introduction of whole slide imaging (WSI), different deep-learning algorithms are significantly contributing to different areas of neurotoxicological analysis like deep-learning-assisted automated brain image segmentation, detection and analysis of toxicity in different regions of the brain, etc . Wang et al used a CNN model to automatically detect focal cerebral ischemia-reperfusion-injured neurons in label-free two-photon microscopic (TPM) images . This model significantly improves diagnostic accuracy compared with standard histology and detects the location of the injured neuron without prior knowledge of histopathology .…”

Section: Deep Learning In Predictive Drug Toxicity Studiesmentioning

confidence: 99%

See 2 more Smart Citations

A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies

Sinha¹,

Ghosh²,

Sil

2023

Chem. Res. Toxicol.

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Drug toxicity prediction is an important step in ensuring patient safety during drug design studies. While traditional preclinical studies have historically relied on animal models to evaluate toxicity, recent advances in deep-learning approaches have shown great promise in advancing drug safety science and reducing animal use in preclinical studies. However, deep-learning-based approaches also face challenges in handling large biological data sets, model interpretability, and regulatory acceptance. In this review, we provide an overview of recent developments in deep-learning-based approaches for predicting drug toxicity, highlighting their potential advantages over traditional methods and the need to address their limitations. Deep-learning models have demonstrated excellent performance in predicting toxicity outcomes from various data sources such as chemical structures, genomic data, and high-throughput screening assays. The potential of deep learning for automated feature engineering is also discussed. This review emphasizes the need to address ethical concerns related to the use of deep learning in drug toxicity studies, including the reduction of animal use and ensuring regulatory acceptance. Furthermore, emerging applications of deep learning in drug toxicity prediction, such as predicting drug–drug interactions and toxicity in rare subpopulations, are highlighted. The integration of deep-learning-based approaches with traditional methods is discussed as a way to develop more reliable and efficient predictive models for drug safety assessment, paving the way for safer and more effective drug discovery and development. Overall, this review highlights the critical role of deep learning in predictive toxicology and drug safety evaluation, emphasizing the need for continued research and development in this rapidly evolving field. By addressing the limitations of traditional methods, leveraging the potential of deep learning for automated feature engineering, and addressing ethical concerns, deep-learning-based approaches have the potential to revolutionize drug toxicity prediction and improve patient safety in drug discovery and development.

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Section: Aaementioning

confidence: 99%

Section: Aaementioning

confidence: 99%

Section: Deep Learning In Predictive Drug Toxicity Studiesmentioning

confidence: 99%

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A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies

Sinha¹,

Ghosh²,

Sil

2023

Chem. Res. Toxicol.

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“…Models can be trained using a deep learning approach to detect degenerated neurons based on their size, colour and shape as shown (manually classified) on the input image fed to the model. Deep CNN model has been used to detect neuronal damage automatically in rat cerebral ischaemic‐reperfusion model (Wang et al, 2020). Images of label‐free brain sections were obtained using two photon microscopy, and a deep learning algorithm was applied to it to detect injured neurons.…”

Section: Ai/deep Learning In Automated Detection and Analysis Of Toxicity In Different Regions Of Brainmentioning

confidence: 99%

Quantitative neurotoxicology: Potential role of artificial intelligence/deep learning approach

Srivastava

Hanig

2020

J of Applied Toxicology

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Neurotoxicity studies are important in the preclinical stages of drug development process, because exposure to certain compounds that may enter the brain across a permeable blood brain barrier damages neurons and other supporting cells such as astrocytes. This could, in turn, lead to various neurological disorders such as Parkinson's or Huntington's disease as well as various dementias. Toxicity assessment is often done by pathologists after these exposures by qualitatively or semiquantitatively grading the severity of neurotoxicity in histopathology slides. Quantification of the extent of neurotoxicity supports qualitative histopathological analysis and provides a better understanding of the global extent of brain damage. Stereological techniques such as the utilization of an optical fractionator provide an unbiased quantification of the neuronal damage; however, the process is time‐consuming. Advent of whole slide imaging (WSI) introduced digital image analysis which made quantification of neurotoxicity automated, faster and with reduced bias, making statistical comparisons possible. Although automated to a certain level, simple digital image analysis requires manual efforts of experts which is time‐consuming and limits analysis of large datasets. Digital image analysis coupled with a deep learning artificial intelligence model provides a good alternative solution to time‐consuming stereological and simple digital analysis. Deep learning models could be trained to identify damaged or dead neurons in an automated fashion. This review has focused on and discusses studies demonstrating the role of deep learning in segmentation of brain regions, toxicity detection and quantification of degenerated neurons as well as the estimation of area/volume of degeneration.

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“…Therefore, the exploration of a new imaging technique that can accurately diagnose ATI during surgery will be of significant clinical meaning. Multiphoton imaging techniques with low cellular damage, high imaging depth, and perform adipose tissue invasion label-free imaging have been demonstrated over the past several years to be a powerful tool for submicron resolution tissue imaging at the cellular and molecular levels [7]. In this study, an attempt was made to identify ATI using MPM based on two-photon excitation fluorescence (TPEF) and second harmonic generation (SHG) signals.…”

Section: Introductionmentioning

confidence: 99%

Monitoring adipose tissue invasion in breast cancer by multiphoton microscopy

Ye¹,

Jiang²,

Zhang³

et al. 2022

Optics in Health Care and Biomedical Optics XII

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Adipocytes are considered to be a critical cell type in the tumor microenvironment of breast cancer. Many studies have confirmed that adipocytes are not only found adjacent to cancer cells, but they also play an active role in the entire process of cancer development, progression, metastasis, and treatment response in breast cancer. Adipose tissue invasion (ATI) is a way of tumor cell metastasis, which not only indicates the poor prognosis of patients but also indicates the decline of survival rate. Multiphoton microscopy (MPM) with subcellular resolution based on second harmonic generation (SHG) and two-photon excited fluorescence (TPEF) is very suitable for real-time detecting morphological and structural changes in biological tissues without tissue staining and exogenous probe molecule. In this study, MPM was applied to identify the adipose tissue invasion in breast cancer patients. The results indicated that it is feasible to detect adipose tissue invasion with multiphoton microscopy, and to provide a new auxiliary tool for pathologists to quickly and effectively diagnose adipose tissue invasion.

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Automated label‐free detection of injured neuron with deep learning by two‐photon microscopy

Cited by 14 publications

References 40 publications

A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies

A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies

Quantitative neurotoxicology: Potential role of artificial intelligence/deep learning approach

Monitoring adipose tissue invasion in breast cancer by multiphoton microscopy

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