Browsing by Author "SILVA, MIGUEL ÂNGELO FERRAZ DA"
Now showing 1 - 1 of 1
Results Per Page
Sort Options
- AI-based synthesis of bacterial colony evolution imagesPublication . SILVA, MIGUEL ÂNGELO FERRAZ DA; Martinho, Diogo Emanuel Pereira; Marreiros, Maria Goreti CarvalhoThe growing demand for safety and efficiency in healthcare highlights the importance of optimising sterilisation procedures, where delays or errors can compromise patient outcomes. In this context, microbiological analysis of agar plates is a fundamental step, as it allows the identification of microbial growth that may compromise sterilisation quality. However, traditional inspection methods are time-consuming and rely heavily on manual observation, which limits their scalability in clinical environments. Meanwhile, Artificial Intelligence has demonstrated strong potential in image analysis and forecasting, offering opportunities to enhance microbiological analysis and support decision-making in healthcare workflows. This dissertation addresses the problem of detecting and predicting the growth of bacterial colonies on agar plates. Anticipating how colonies evolve is essential to evaluate contamination levels, yet this task remains challenging due to the natural variability of growth patterns, the occurrence of overlapping colonies, and the diversity of experimental conditions that affect microbial behaviour. To tackle this problem, an integrated application was developed and structured into three main modules. The first is a detection module that applies the YOLO object detection architecture to identify bacterial colonies from agar plate images. The second is a synthetic forecasting module based on convolutional autoencoders capable of predicting future colony states from early observations. The third is a contamination analysis module that translates predictions into interpretable indicators such as colony count, average size, growth rate, and coverage. Together, these modules form a complete pipeline designed to combine visual fidelity with biological relevance. The results show that the system can detect colonies with high accuracy, achieving a Precision of 99.1%, a Recall of 91.7%, and an F1 score of 95.3%. In addition, the forecasting module generated realistic predictions of colony growth, and the contamination analysis provided meaningful metrics across different experimental conditions. The exploration of different temporal intervals revealed complementary trade-offs between predictive detail and biological plausibility, reinforcing the flexibility of the proposed methodology. The main conclusion of this dissertation is that Artificial Intelligence can be effectively applied to predict microbial growth in laboratory settings. By integrating detection, forecasting, and contamination analysis within a single framework, this work establishes a technological foundation that supports the transition to more intelligent sterilisation workflows and contributes to the broader vision of safe, efficient, and smart healthcare environments.