# Technique Evolutions for Microorganism Detection in Complex Samples: A Review
## 3 Developments in Innovative Detection Methods
### Development of Physical and Computer Analysis Methods
#### 3.3.2 Deep Learning for Microscopy-Based Sampling Methods  
Recent progress in artificial intelligence, and more specifically deep learning, opened the way to an automatic and robust analysis of images. This is also true for the analysis of microscopic images, and deep learning was applied to the detection and identification of microorganisms [103; 104].  
Deep learning is part of the larger family of artificial neural networks (ANNs). ANNs were inspired by the functioning of the brain, with neurons organized in successive layers connected between them (Figure 7), from the input (e.g., images) to the output (e.g., bacteria presence). By training them on labelled data (e.g., image of known bacteria), they can find patterns allowing them to recognize unknown data afterwards. Deep learning refers to ANNs with many consecutive layers and corresponding ANNs are called deep neural networks (DNNs). Such structure was only recently enabled from the development of computational power.  
In the case of image analysis, DNNs are capable of extracting shape features of images to classify them according to pre-defined labels (e.g., different species of bacteria), or to extract a quantity of an image (e.g., a concentration in bacteria). In practically all fields of image analysis, deep learning has proved to be more accurate than other methods, and even more accurate than human eyes for some applications [105]. Deep learning is mainly used for classification of objects, and in the field of microbiology, to identify various species of virus [106], bacteria [107; 108], fungi [109], or parasites [110]. Those studies include the analysis of different modalities of microscopy such as electron microscopy (for viruses), fluorescence microscopy, brightfield microscopy, or even vibrational microspectroscopy. Trained on labelled datasets, deep neural networks are then able to identify microorganisms based on their shape or spectrum for spectroscopic images.  
For detection purposes, DNNs can detect automatically bacteria over a large field of view, and distinguish them against other particles. [PERSON] et al. combined several DNNs to detect and identify single cells of five strains of bacteria (_Campylobacter_, _E. coli_, _Listeria_, _Salmonella_, and _Staphylococcus_) from visible spectroscopy images [111]. [PERSON] et al. [112] used a DNN to detect bacterial growth inside an agar plate from coherent microscopy images taken every 30 min. Three strains (_E. coli_, _Klebsiella aerogenes_, and _Klebsiella pneumoniae_) were tested for detection and subsequent identification, with an LOD of \(\sim\)1 CFU/L in less than 9 h.  
Deep learning allows for a finer analysis of images for increased sensitivity and specificity in detection. Public databases of different types of microorganisms are emerging [103], which allows a broader use of deep learning for microbiology. For now, studies are mainly  
Figure 7: Diagram of deep learning applied to the detection and identification of microorganisms in microscopic images.  
carried out on isolates, but it would be interesting to extend on bacteria within complex environments.  
In conclusion, while Raman spectroscopy took a long time to analyze and process data in the past, it is becoming fast and very sensitive thanks to deep learning. If we witness the birth of an official reference system for microorganisms, the Raman method will become a must in the quality control of ATMP. However, its purchase cost may remain an obstacle to its acquisition for some laboratories.