RESUMO
This study showcases a novel AI-driven approach to accurately differentiate between stage one and stage two gastric carcinoma based on pathology slide analysis. Gastric carcinoma, a significant contributor to cancer-related mortality globally, necessitates precise staging for optimal treatment planning and patient management. Leveraging a comprehensive dataset of 3540 high-resolution pathology images sourced from Kaggle.com, comprising an equal distribution of stage one and stage two tumors, the developed AI model demonstrates remarkable performance in tumor staging. Through the application of state-of-the-art deep learning techniques on Google's Collaboration platform, the model achieves outstanding accuracy and precision rates of 100%, accompanied by notable sensitivity (97.09%), specificity (100%), and F1-score (98.31%). Additionally, the model exhibits an impressive area under the receiver operating characteristic curve (AUC) of 0.999, indicating superior discriminatory power and robustness. By providing clinicians with an efficient and reliable tool for gastric carcinoma staging, this AI-driven approach has the potential to significantly enhance diagnostic accuracy, inform treatment decisions, and ultimately improve patient outcomes in the management of gastric carcinoma. This research contributes to the ongoing advancement of cancer diagnosis and underscores the transformative potential of artificial intelligence in clinical practice.
RESUMO
This study presents an innovative application of artificial intelligence (AI) in distinguishing dermoscopy images depicting individuals with benign and malignant skin lesions. Leveraging the collaborative capabilities of Google's platform, the developed model exhibits remarkable efficiency in achieving accurate diagnoses. The model underwent training for a mere one hour and 33 minutes, utilizing Google's servers to render the process both cost-free and carbon-neutral. Utilizing a dataset representative of both benign and malignant cases, the AI model demonstrated commendable performance metrics. Notably, the model achieved an overall accuracy, precision, recall (sensitivity), specificity, and F1 score of 92%. These metrics underscore the model's proficiency in distinguishing between benign and malignant skin lesions. The use of Google's Collaboration platform not only expedited the training process but also exemplified a cost-effective and environmentally sustainable approach. While these findings highlight the potential of AI in dermatopathology, it is crucial to recognize the inherent limitations, including dataset representativity and variations in real-world clinical scenarios. This study contributes to the evolving landscape of AI applications in dermatologic diagnostics, showcasing a promising tool for accurate lesion classification. Further research and validation studies are recommended to enhance the model's robustness and facilitate its integration into clinical practice.
