Rapid and precise bacterial identification is pivotal in modern healthcare for numerous reasons. Firstly, it dramatically enhances treatment efficacy in critical conditions such as sepsis, where every hour count1. Timely and accurate identification of the causative bacterial agent enables clinicians to promptly administer the most effective antibiotics, thereby significantly improving patient survival rates. Furthermore, the ability to identify bacteria quickly plays a vital role in combating antibiotic resistance. By reducing the need for broad-spectrum antibiotics, which are often used when the causative agent is unknown, targeted therapy based on precise bacterial identification helps preserve the efficacy of these critical drugs. This targeted approach also minimizes the risk of antibiotic misuse, a major factor in the development of antibiotic-resistant strains of bacteria2. Economically, rapid bacterial identification can lead to significant healthcare savings. By enabling quicker diagnosis and treatment, it can reduce the length of hospital stays and the associated costs3. This efficiency not only benefits patients but also helps healthcare systems manage resources more effectively. In terms of patient safety, rapid and precise identification minimizes the risk of adverse reactions associated with inappropriate antibiotic use. In summary, effective bacterial identification is a fundamental aspect of contemporary healthcare, playing a critical role in improving clinical outcomes, preventing the spread of infections, reducing healthcare costs, enhancing patient safety, and aiding the global fight against antibiotic resistance.Various bacterial detection techniques have both specific benefits and drawbacks. Traditional culture methods are dependable but slow, often requiring several days for conclusive results4. Spectroscopic techniques such as Near-infrared (NIR) and Raman spectroscopy specify macromolecular composition of bacterial cells, such as nucleic acids, proteins, carbohydrates, and fatty acids, providing distinct absorption spectra5. However, the challenge in microbial spectroscopy lies in the fact that most microorganisms have similar chemical compositions, resulting in very similar spectra. Polymerase Chain Reaction (PCR) is utilized to detect bacterial pathogens by targeting specific DNA sequences with specific primers. Higher sensitivity is offered by PCR compared to traditional culture and staining methods6, and the process is completed within a few hours7. However, certain drawbacks are associated with PCR: its specificity may be lower, which increases the risk of false positives8. Additionally, because specific primers are necessary for identifying different microorganisms, potential pathogens must often be identified by physicians before performing selective PCR. Sequencing methods, particularly Whole Genome Sequencing (WGS), play a crucial role in providing detailed information on bacterial species, proving instrumental in identifying pathogens, detecting antimicrobial-resistant genes, and tracing bacterial outbreaks9. A key advantage of WGS over PCR is its lack of requirement for specific targeting, which means it does not need constant updates in primer development in response to bacterial mutations. Next-Generation Sequencing (NGS) has advanced this technology by enabling the simultaneous sequencing of millions of fragments in a single run, facilitating microscale reactions on a chip10. Despite these advancements, challenges persist in sample handling, sequencing, and data analysis, with potential for errors and biases11. Matrix-Assisted Laser Desorption Ionization-Time-of-Flight mass spectrometry (MALDI-TOF MS) differentiates microorganisms by their unique protein profiles, primarily ribosomal and housekeeping proteins12. It generates a Peptide Mass Fingerprint (PMF) for identification by comparing spectra with reference databases. While cost-effective and rapid, MALDI-TOF MS requires significant initial investment and ongoing maintenance. Drawbacks include difficulty in distinguishing closely related bacteria and variability in reference databases and scoring algorithms among manufacturers13. These advanced methods are not only complex but also demand specialized skills for their operation and data analysis. Furthermore, such sophisticated equipment might not be readily available in all laboratories, especially those with limited resources.Bacterial rapid detection using optical scattering technology (BARDOT), a noninvasive and label-free system, utilized forward light scattering combined with an advanced image analysis system to differentiate distinct scattering patterns among various microorganisms14. This technique achieved 91–100% accuracy in differentiating different species. It effectively detected and identified bacterial colonies from various genera such as Escherichia, Salmonella, and Listeria. BARDOT can distinguish between bacterial micro-colonies within 6 h after plate streaking15,16. Enhancements in BARDOT’s capabilities include the integration of Digital In-Line holographic microscopy (DIHM)17. This enhancement analyzed both amplitude and phase properties of bacterial colonies enabling identification of bacteria with nearly 99% accuracy. Recently, as artificial intelligence algorithms have gained popularity, they have been integrated into the holographic identification of biological cells. A label-free sensor using DIHM combined with machine learning algorithms was proposed to automatically classify erythrocytes, achieving high accuracy with a decision tree model18. This methodology is effectively used to detect abnormal erythrocytes and supports the computer-aided diagnosis of hematological diseases. A miniaturized holographic imaging system was developed for lens-free imaging of white blood cells19. This system automated data analysis and classification to ensure accuracy. Ground truth was established using reference holographic images, and features were extracted with machine learning algorithms to achieve 99% classification accuracy. An automatic label-free detection of unstained malaria-infected red blood cells using DIHM combined with machine learning algorithms was proposed20. The highest accuracy was achieved by the support vector machine (SVM) model, with healthy and malaria-infected RBCs being accurately distinguished with 97% accuracy. DIHM was also utilized for tracking the three-dimensional swimming of motile microorganisms, which enabled species identification21. Real-time analysis on single-board computers was facilitated using a common neural network, allowing for rapid localization of cells in three dimensions as they swam. With AI assistance and DIHM enables high-throughput detection and enumeration of biological cells, removing barriers like specialized knowledge and manual interpretation while enhancing accuracy and efficiency. However, this technology relies on a coherent light source that necessitates a complex optical setup, which is expensive and sensitive to optical alignment, susceptible to environmental factors such as vibrations.To simplify the optical setup, we employ RGB light emitting diodes (LED) as a light source. Traditionally, LEDs are considered partially coherent light sources22. Their limited temporal and spatial coherence have been perceived as a disadvantage. While temporal coherence affects speckle formation, spatial coherence can be enhanced with spatial filters (e.g., pinholes). In 2017, a study revealed a linear relationship between image sharpness and spatial coherence23. This coherence can be improved by adjusting the light source size and propagation distance. It was suggested that a light source size under 300 microns achieves sharp holographic images, even with short propagation distances. Many modern LED modules, such as the SMD5050, feature individual LEDs that meet this size requirement. This makes them suitable and cost-effective light sources for obtaining bacterial colony scattering patterns. Moreover, the use of commercial LED modules eliminates the need for complicated optical setups and increases their potential applications.In this present work, an object detection model has been also integrated for image analysis. Particularly, the You Only Look Once (YOLO) model24,25, which is renowned for both its precision and speed, is employed. YOLO’s accessibility is enhanced across various platforms, from edge devices to the cloud, while maintaining computational cost-effectiveness. YOLO’s success in diverse fields such as computer vision, plant science, and medical research, including the detection of blood cells, cancer, tumors, and bone fractures26,27,28,29,30,31,32,33, highlights its potential for our purposes. In addition to YOLO, RCNN and Fast RCNN were considered. RCNN and Fast RCNN are effective at detecting small objects but are slower and unsuitable for real-time detection34. RCNN involves a region proposal process followed by classification, resulting in high accuracy but increased computational time35. Fast RCNN integrates these steps for improved efficiency but still lacks real-time performance36. YOLO was chosen for its balance of speed and accuracy, essential for real-time analysis. Unlike RCNN and Fast RCNN, YOLO processes images in a single pass, providing high efficiency and suitability for dynamic environments. YOLO’s real-time capabilities and streamlined workflow make it a practical choice for this research. Although deep learning models such as YOLO can provide accurate outputs, due to their inherent opacity, their decision-making processes cannot be directly interpreted by human intuition. To gain insight into the decision-making processes of the YOLOv8 models, Eigen-CAM was used to highlight the regions of an image that contributed significantly to the prediction37. A class activation map (CAM) is generated by Eigen-CAM using the first principal components (PC1) of feature maps, which capture the most significant variance in the data. The CAM visually indicates critical regions of the image for the CNN’s predictions, presenting a heatmap overlay that illustrates areas of high activation within the input image. Eigen-CAM’s versatility is notable, as it can be universally applied across all CNN models without requiring modifications to layers or retraining.To demonstrate the capability of our approach in the detection and identification of both Gram-positive and Gram-negative, as well as closely related species, four bacterial strains were selected: Escherichia coli ATCC 11775, E. coli ATCC 25922, Staphylococcus aureus ATCC 25923, and S. aureus MRSA ATCC 25923. E. coli, which are Gram-negative species, are commonly used in microbiology laboratories. S. aureus, on the other hand, are Gram-positive species. MRSA (Methicillin-resistant Staphylococcus aureus) is a significant health concern. This Gram-positive bacterium is one of the primary contributors to the emergence of infections that are challenging to treat due to its innate resistance to drugs38. The complexity of MRSA treatment and its classification as a priority pathogen by the World Health Organization (WHO) is due to its resistance to β-lactams39. Although molecular diagnostics such as PCR are employed for MRSA detection, their cost and complexity limit their widespread use40.This study introduces an enhanced bacteria detection system based on optical scattering that incorporates a simplified optical arrangement using a commercial RGB LED module, and a detection model using YOLOv8. We demonstrats for the first time that LED-generated diffraction patterns of bacterial colonies can be successfully used for bacterial identification. An image registration algorithm was employed to improve diffraction image quality. The integration of Arduino and YOLOv8 automated the analysis process, enabling the detection and identification of bacterial strains. Three YOLOv8 model sizes were evaluated to determine the optimal variant for our application. The effectiveness of this LED-YOLOv8 approach is demonstrated by accurately detecting and identifying four bacterial species: E. coli ATCC 11775, E. coli ATCC 25922, S. aureus ATCC 25923, and S. aureus MRSA 1320. Eigen-CAM was employed to gain insight into the decision-making processes of the model during prediction.
