About this Research Topic
In the realm of healthcare, digestive diseases such as inflammatory bowel disease (IBD), irritable bowel syndrome (IBS), and various neoplasms pose significant global health challenges. Early diagnosis and precise management are critical yet difficult aspects of precision medicine for these diseases. Traditional diagnostic methods such as endoscopy and histopathology, while effective, are invasive and provide limited diagnostic abilities. Conventional imaging techniques like CT, MRI, and ultrasound are critical for anatomical visualization but often fall short in early detection and comprehensive disease monitoring.
Recent advancements in artificial intelligence (AI), particularly through machine learning (ML) and deep learning (DL), are revolutionizing medical imaging in the diagnosis and management of digestive diseases. These AI-powered tools can proficiently analyse extensive datasets from multiple sources with remarkable speed and precision, uncovering patterns and anomalies that might be overlooked by human experts. By integrating data from imaging, genetic, biochemical analyses, and other sources of medical information, AI not only enhances diagnostic accuracy but also facilitates personalized treatment planning. Consequently, this approach is crucial for the early diagnosis and effective management of various digestive diseases. AI-driven insights contribute to accurate disease staging, monitoring therapeutic responses, and tailoring treatments to individual patient profiles, ultimately improving overall patient outcomes. Despite challenges with model generalizability and ethical issues, continuous research and collaboration are essential to fully leverage AI's potential in improving digestive health management.
This research topic aims to attract high-quality papers that showcase the latest advancements in medical imaging methodologies and their applications in diagnosing and treating digestive diseases. By integrating biomedicine with cutting-edge information technology and leveraging AI, this topic emphasizes the use of ML and DL to automatically extract complex features from medical images, eliminating the need for manual extraction by experts.
To further advance our understanding in this field, we invite contributions covering, but not limited to, the following themes:
● Automated detection of digestive system diseases: Use ML and DL to improve the diagnostic accuracy particularly in the early detection of cancers where they outpace traditional manual methodologies.AI Theory and Methods: Development and improvement of AI methods, including clinical decision support systems specifically related to digestive diseases.
● Accurate Staging of Digestive Diseases: Applications of ML and DL that provide more precise staging, helping clinicians assess disease progression and tumor spread.
● Personalized Treatment Plans Based on Multimodal Data Fusion: Studies that integrate imaging, genomic, and clinical data using ML and DL to design personalized treatment plans. This includes predicting the outcomes of various treatment options and optimizing the selection of surgery, chemotherapy, radiotherapy, or immunotherapy.
● Prediction of Recurrence and Survival: Research showcasing advanced AI technologies to predict recurrence risks and survival probabilities for patients with digestive system tumours. This aids in developing follow-up plans and optimizing long-term management strategies.
This scope aims to propel the field of precision medicine by tapping into AI's versatility in adapting rapidly to varying tasks and data types, thus fostering robust, patient-specific medical treatment paradigms within digestive healthcare.
Recent advancements in artificial intelligence (AI), particularly through machine learning (ML) and deep learning (DL), are revolutionizing medical imaging in the diagnosis and management of digestive diseases. These AI-powered tools can proficiently analyse extensive datasets from multiple sources with remarkable speed and precision, uncovering patterns and anomalies that might be overlooked by human experts. By integrating data from imaging, genetic, biochemical analyses, and other sources of medical information, AI not only enhances diagnostic accuracy but also facilitates personalized treatment planning. Consequently, this approach is crucial for the early diagnosis and effective management of various digestive diseases. AI-driven insights contribute to accurate disease staging, monitoring therapeutic responses, and tailoring treatments to individual patient profiles, ultimately improving overall patient outcomes. Despite challenges with model generalizability and ethical issues, continuous research and collaboration are essential to fully leverage AI's potential in improving digestive health management.
This research topic aims to attract high-quality papers that showcase the latest advancements in medical imaging methodologies and their applications in diagnosing and treating digestive diseases. By integrating biomedicine with cutting-edge information technology and leveraging AI, this topic emphasizes the use of ML and DL to automatically extract complex features from medical images, eliminating the need for manual extraction by experts.
To further advance our understanding in this field, we invite contributions covering, but not limited to, the following themes:
● Automated detection of digestive system diseases: Use ML and DL to improve the diagnostic accuracy particularly in the early detection of cancers where they outpace traditional manual methodologies.AI Theory and Methods: Development and improvement of AI methods, including clinical decision support systems specifically related to digestive diseases.
● Accurate Staging of Digestive Diseases: Applications of ML and DL that provide more precise staging, helping clinicians assess disease progression and tumor spread.
● Personalized Treatment Plans Based on Multimodal Data Fusion: Studies that integrate imaging, genomic, and clinical data using ML and DL to design personalized treatment plans. This includes predicting the outcomes of various treatment options and optimizing the selection of surgery, chemotherapy, radiotherapy, or immunotherapy.
● Prediction of Recurrence and Survival: Research showcasing advanced AI technologies to predict recurrence risks and survival probabilities for patients with digestive system tumours. This aids in developing follow-up plans and optimizing long-term management strategies.
This scope aims to propel the field of precision medicine by tapping into AI's versatility in adapting rapidly to varying tasks and data types, thus fostering robust, patient-specific medical treatment paradigms within digestive healthcare.
Keywords: Precision Medicine; Machine Learning; Deep learning; Medical imaging; Digestive diseases
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
