## 2 Related Works
The benefits of automation offered by these systems include enhanced standardization, leading to greater accuracy, improved data management, with a simultaneous decrease in transcription errors, quicker availability of results, and reduced risk of exposure to cultivated pathogens. Archived images of culture plates have increased staff productivity, enabling more comprehensive quality reviews and the creation of real-world training sets. Moreover, it is possible to use the image library as a training tool [9]. The growth of E. coli bacteria was observed by analyzing images during 24 h of incubation [11]. Changes in the disk inhibition zones over time represented positive results in terms of preventing contamination in environments where microbiological processes are carried out manually with image processing techniques. With this method, zone measurements were carried out according to the reference disk diameter (6 mm), but no information on antibiogram types was given. In addition, [PERSON] et al. have noted that the study by [PERSON] et al. [12], which used convolutional neural networks (CNNs) to count colonies, reported 92.8% accuracy counts. Utilizing their developed method, quantitative data were obtained, and contamination problems were prevented. However, it was emphasized that the study should be repeated for different types of bacteria and antibiotics [11]. Various AI approaches have been developed that have had success in microbiology. [PERSON] et al. [13] briefly explained how AI could address antimicrobial resistance (AMR) through predictive modeling, smart antibiotic use, and the exploration of antimicrobial peptides and antibiotic combinations. In [14], [PERSON] et al. systematically searched for studies that use machine learning algorithms to improve species identification and susceptibility testing based on MALDI-TOF mass spectra [15]. A total of thirty-six studies were identified that utilized various machine learning algorithms to identify species and support antimicrobial resistance profiling. There was a significant overlap in the pathogens studied and the types of machine learning algorithms used. However, a quality assessment revealed several shortcomings in current machine learning implementations, which need to be addressed before routine diagnostic use can be considered. [PERSON] et al. [16] developed an offline mobile application technology which analyzed disk diffusion ASTs and presented interpreted results to users. The methodology of this study was described as combining machine learning (ML) and traditional image processing algorithms for automated AST analysis using a mobile application. The OPENCV library from C++ libraries was used to detect the inhibition zones and the Tensor Flow library from ML libraries was used for antibiogram detection. In the experimental section, several problematic images were detected in the datasets, due to damaged plates and low contrast. The difficulty of isolating some inhibition zones, even by eye, was mentioned. In comparison, the combined effect of bacterial pigmentation and ambient illumination introduced a significant degree of variability in the bacterial inhibition intensity contrast. In the experiments, images with low contrast gave worse results. Consequently, for each of the two tests, a neural network model was trained to categorize the images as positive or negative. Accuracy rates of 99.7% and 98% were achieved with models identifying clindamycin-inducible resistance and extended-spectrum beta-lactamases (ESBLs), respectively. However, the models exhibited suboptimal performance across a diverse range of images, including variations in plate layout and bacterial texture. Given the potential seriousness of classification errors in interpreting antibiotic susceptibility testing (AST) and subsequent patient treatment, the developed methods incorporated a user confirmation step within the automatic detection of the resistance mechanism.