Feedback Microscopy (FDBKM)

Biological systems are inherently complex, and the response of even the more stable model can be highly heterogeneous. Therefore, many samples must be imaged to achieve statistically meaningful results. In microscopy, obtaining sufficiently large quantitative datasets during manual operation can become very time-consuming because the operator must first identify the targets of interest and then acquire each with experiment-specific settings. This workflow imposes limits on the number of events that can be recorded and the reproducibility of the results, especially when the phenotypes of interest are rare or occur only during specific biological stages. Feedback microscopy or smart microscopy allows automation of the image acquisition process, often in combination with responsive image analysis to find the sites of interest. This method can also play a key role in driving unbiased imaging of specimens.
In many cases, AI or deep learning are used in the image analysis and object recognition parts of the feedback microscopy process.