How are AI and ML Revolutionizing Microscopy?

By Ankit SinghReviewed by Susha Cheriyedath, M.Sc.May 13 2024

Microscopy is the scientific study of objects and details too small to be seen with the naked eye. It has been a fundamental tool for many scientific disciplines, including biology, medicine, materials science, and nanotechnology.

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However, the increasing complexity of microscopy data and human analysis limitations have necessitated a new approach. The emergence of artificial intelligence (AI) and machine learning (ML) has significantly enhanced the capabilities and applications of microscopy, bringing about a revolution in research and industry practices.

This article explores the evolution of AI and ML in microscopy and discusses its current impact, latest developments, and challenges in the field.

Evolution of AI and ML in Microscopy

Integrating AI and ML into microscopy has been a gradual but transformative process. In the past, microscopy relied on manual observation and analysis, which was time-consuming and prone to human error. However, with the advent of computational methods, advanced algorithms, and the availability of large-scale microscopy datasets, researchers began exploring ways to automate and enhance various aspects of microscopy.¹

In its early stages, AI was mainly used for image analysis tasks like segmentation and feature recognition. However, the real breakthrough came with the advent of deep learning, a subfield of AI that takes inspiration from the structure and function of the human brain.

Deep learning algorithms are particularly adept at recognizing patterns in complex imagery, making them an excellent tool for analyzing microscopy data. They can analyze large-scale microscopy data at an unprecedented speed, which opens the door to more sophisticated applications, such as real-time imaging and automated cell tracking.¹

Revolutionizing Image Analysis

One of AI and ML's primary contributions to microscopy is the automation of image analysis tasks. Unlike traditional methods, AI-powered image segmentation techniques can automatically segment images, accurately separating objects of interest from the background.

A recent study published in Scientific Reports shows how AI can outperform traditional methods in segmenting complex biological structures within electron microscopy images. Automating these tasks reduces the risk of bias that may arise from manual analysis and frees up valuable researcher time.²

AI has also shown remarkable capabilities in image classification tasks. By training deep learning models on extensive datasets of labeled microscopy images, scientists can create algorithms that can automatically classify cells, recognize specific disease markers, and even distinguish between different types of bacteria.

A recent report demonstrated the potential of AI in precisely classifying different subtypes of lung cancer in tissue microscopy images. This can revolutionize disease diagnosis by enabling faster, more objective, and potentially more cost-effective analysis.³

In addition, recent advancements in convolutional neural networks (CNNs), a type of deep learning algorithm, have significantly improved the accuracy of image segmentation and classification. Recently, researchers used a CNN-based approach to automatically segment and classify subcellular structures in microscopy images. They achieved results equivalent to expert manual annotations.⁴

Real-time Imaging and Analysis

AI and ML have revolutionized microscopy by enabling real-time imaging and analysis. With this technology, researchers can observe dynamic biological processes with unprecedented detail and temporal resolution. By leveraging computational models and predictive algorithms, scientists can extract valuable insights from live microscopy data in real-time, facilitating a deeper understanding of complex biological phenomena.

In a recent study, researchers developed a novel framework for real-time analysis of neuronal activity using AI-driven microscopy. The study combined advanced imaging techniques with machine learning algorithms to monitor and predict neuronal dynamics with high spatiotemporal resolution, opening new avenues for neuroscience research.⁵

Extracting New Insights: Feature Engineering and Beyond

The role of AI in microscopy extends beyond simply automating existing tasks. AI algorithms can also help with feature engineering, which involves identifying and extracting new features from complex microscopy data.

A recent Scientific Reports study explains how AI can be used to discover subtle morphological changes in cells that could be crucial in understanding disease progression. The ability to detect previously unrecognized patterns presents exciting opportunities for groundbreaking discoveries in various fields of biology and medicine.⁶

Integration with Multimodal Imaging

Another area where AI and ML have significantly contributed to microscopy is the integration of multimodal imaging modalities. Multimodal imaging techniques combine fluorescence, electron microscopy, and atomic force microscopy to provide comprehensive information and enhance overall imaging capabilities.

Recently, researchers demonstrated a multimodal imaging platform that combines super-resolution fluorescence microscopy with electron microscopy for comprehensive cellular imaging. The platform integrates AI-based image registration and fusion algorithms, allowing seamless data integration from different modalities. This integration enables researchers to correlate molecular and structural information with unprecedented levels of detail.⁷

AI in Super-Resolution Microscopy

Super-resolution microscopy techniques allow scientists to visualize biological structures at resolutions exceeding the diffraction limit of light. However, these techniques often generate complex and noisy data, making interpreting them difficult. AI can play a vital role in improving the quality of super-resolution microscopy images by reducing noise and reconstructing high-fidelity representations of biological structures.

A recent Nature report demonstrated how deep learning enhances the resolution and quality of super-resolution microscopy data. This integration of AI with super-resolution microscopy has immense potential for uncovering the intricate details of cellular organization and function.⁸

Artificial Confocal Microscopy: A Breakthrough in Label-Free Imaging

Traditional wide-field microscopy of optically thick specimens often suffers from reduced contrast due to spatial cross-talk, limiting depth resolution. While modern laser scanning confocal fluorescence microscopy offers high-depth resolution, it is plagued by photobleaching and phototoxicity.

To address this issue, researchers at the University of Illinois at Urbana-Champaign have developed artificial confocal microscopy (ACM), which achieves confocal-level depth sectioning and sensitivity non-destructively on unlabeled specimens.⁹

By combining a commercial laser scanning confocal instrument with a quantitative phase imaging module and training a convolutional neural network, ACM produces depth-sectioned images with enhanced contrast and specificity. This enables the recovery of confocal-like tomographic volumes from various specimens. It offers dynamic, quantitative data from thick samples while preserving chemical specificity through computational methods.

Accelerating Drug Discovery and Material Science

Due to its high speed and accuracy, AI-powered microscopy analysis is extremely beneficial in drug discovery pipelines. AI can significantly accelerate the identification of promising therapeutic agents, allowing researchers to quickly screen large libraries of potential drug candidates on cellular models. Recently, AI-driven microscopy helped identify new drug targets for a previously untreatable form of leukemia.¹⁰

AI is also transforming material science by enabling high-throughput characterization of novel materials at the microscopic level. It can help analyze electron microscopy images of new materials, leading to the development of advanced materials with customized properties.¹¹

Challenges and Ethical Considerations

While AI and ML have made significant advances in microscopy, several challenges and ethical considerations remain. One major challenge is the interpretability of AI-driven analyses, as complex neural networks often function as black boxes, making it difficult to understand how they arrive at their conclusions. This lack of transparency can hinder scientific reproducibility and raise concerns about algorithmic bias and error propagation.¹

Reliance on AI and ML algorithms also presents new data privacy and security risks. Microscopy datasets, especially those derived from biomedical research, may contain sensitive information about individuals or organisms. Therefore, robust data governance frameworks and privacy-preserving techniques are essential to safeguard against unauthorized access and misuse of microscopy data.¹

Future Prospects 

AI and ML hold great potential to enhance our understanding of the microscopic world and accelerate scientific discovery across various domains. As computational methods and hardware capabilities evolve, further innovations in microscopy technology can be anticipated, leading to new applications and insights.

Future developments may involve refining AI algorithms for faster and more accurate image analysis. AI-driven robotic systems could be integrated for automated sample preparation and experimentation. Advancements in deep learning techniques, such as reinforcement learning and unsupervised learning, may further improve the capabilities of AI-powered microscopy systems, enabling discovery in previously unexplored realms of the microscopic world.

In conclusion, the integration of AI and ML into microscopy is transforming the field, leading to a new era of automation, efficiency, and discovery potential. AI is revolutionizing image analysis and feature extraction and accelerating progress in drug discovery, material science, and other scientific disciplines.

As AI algorithms continue to evolve and collaborative efforts address existing challenges, their integration into microscopy is set to unlock groundbreaking scientific discoveries and translate those insights into tangible benefits for humanity.

More from AZoOptics: Attosecond Spectroscopy: Advancements in Ultrafast Molecular Dynamics

References and Further Reading

1. von Chamier, L., Laine, RF., Henriques, R. (2019). Artificial intelligence for microscopy: what you should know. Biochemical Society Transactions. doi.org/10.1042/bst20180391
2. Gómez-de-Mariscal, E., Maška, M., Kotrbová, A. et al. (2019). Deep-Learning-Based Segmentation of Small Extracellular Vesicles in Transmission Electron Microscopy Images. Sci Rep. doi.org/10.1038/s41598-019-49431-3
3. Wang, S., Yang, DM., Rong, R., Zhan, X., Fujimoto, J., Liu, H., Minna, J., Wistuba, II., Xie, Y., Xiao, G. (2019). Artificial Intelligence in Lung Cancer Pathology Image Analysis. Cancers. doi.org/10.3390/cancers11111673
4. Hemalatha, B. (2022). Deep learning approach for segmentation and classification of blood cells using enhanced CNN. Measurement: Sensors. /doi.org/10.1016/j.measen.2022.100582
5. Vizcaino, JP., et al. (2021). Real-Time Light Field 3D Microscopy via Sparsity-Driven Learned Deconvolution. 2021 IEEE International Conference on Computational Photography (ICCP). doi.org/10.1109/ICCP51581.2021.9466256
6. Chang, A., Prabhala, S., Daneshkhah, A. et al. (2024). Early screening of colorectal cancer using feature engineering with artificial intelligence-enhanced analysis of nanoscale chromatin modifications. Sci Rep. doi.org/10.1038/s41598-024-58016-8 
7. Subramanian, R., Tang, R., Zhang, Z. et al. (2022). Multimodal NASH prognosis using 3D imaging flow cytometry and artificial intelligence to characterize liver cells. Sci Rep. doi.org/10.1038/s41598-022-15364-7
8. Fang, L., Monroe, F., Novak, SW. et al. (2021). Deep learning-based point-scanning super-resolution imaging. Nat Methods. doi.org/10.1038/s41592-021-01080-z
9. Chen, X., Kandel, ME., He, S. et al. (2023). Artificial confocal microscopy for deep label-free imaging. Nat. Photon. doi.org/10.1038/s41566-022-01140-6
10.  Tran, NL., Kim, H., Shin, CH. et al. (2023). Artificial intelligence-driven new drug discovery targeting serine/threonine kinase 33 for cancer treatment. Cancer Cell Int. doi.org/10.1186/s12935-023-03176-2 
11. Baskaran, A., Kautz, EJ., Chowdhary, A. et al. (2021). Adoption of Image-Driven Machine Learning for Microstructure Characterization and Materials Design: A Perspective. JOM. doi.org/10.1007/s11837-021-04805-9

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