Artificial intelligence(AI)is significantly advancing precision medicine,particularly in the fields of immunogenomics,radiomics,and pathomics.In immunogenomics,AI can process vast amounts of genomic and multi-omic dat...Artificial intelligence(AI)is significantly advancing precision medicine,particularly in the fields of immunogenomics,radiomics,and pathomics.In immunogenomics,AI can process vast amounts of genomic and multi-omic data to identify biomarkers associated with immunotherapy responses and disease prognosis,thus providing strong support for personalized treatments.In radiomics,AI can analyze high-dimensional features from computed tomography(CT),magnetic resonance imaging(MRI),and positron emission tomography/computed tomography(PET/CT)images to discover imaging biomarkers associated with tumor heterogeneity,treatment response,and disease progression,thereby enabling non-invasive,real-time assessments for personalized therapy.Pathomics leverages AI for deep analysis of digital pathology images,and can uncover subtle changes in tissue microenvironments,cellular characteristics,and morphological features,and offer unique insights into immunotherapy response prediction and biomarker discovery.These AI-driven technologies not only enhance the speed,accuracy,and robustness of biomarker discovery but also significantly improve the precision,personalization,and effectiveness of clinical treatments,and are driving a shift from empirical to precision medicine.Despite challenges such as data quality,model interpretability,integration of multi-modal data,and privacy protection,the ongoing advancements in AI,coupled with interdisciplinary collaboration,are poised to further enhance AI’s roles in biomarker discovery and immunotherapy response prediction.These improvements are expected to lead to more accurate,personalized treatment strategies and ultimately better patient outcomes,marking a significant step forward in the evolution of precision medicine.展开更多
The automatic classification of thyroid nodules in ultrasound images is a critical research focus in medical imaging.However,publicly available thyroid ultrasound datasets remain scarce.In this study,we developed the ...The automatic classification of thyroid nodules in ultrasound images is a critical research focus in medical imaging.However,publicly available thyroid ultrasound datasets remain scarce.In this study,we developed the Ultrasound Dataset for Thyroid Nodules(UD-TN),a comprehensive dataset containing 10,495 labeled images classified as benign or malignant based on pathology-confirmed results.To establish a benchmark,we proposed the Thyroid Ultrasound Image Neural Network(ThyUNet),a deep learning model designed for accurate nodule classification.By incorporating high-resolution feature enhancement,instance normalization,and dilated convolutions into residual blocks,ThyUNet excels in extracting fine-grained features,particularly for small nodules.Experimental results demonstrate that ThyUNet achieves state-of-the-art performance,with an accuracy of 89.7%,a sensitivity of 0.879,and a specificity of 0.910 on the testing set.These results surpass those of other advanced architectures,highlighting the model’s effectiveness.UD-TN and ThyUNet contribute significantly to advancing intelligent medical diagnostics.Dataset details and access instructions are available at https://github.com/18811755633/Sample-of-UD-TN.展开更多
基金supported by grants from the National Natural Science Foundation of China(Grant No.82272008)The Science&Technology Development Fund of Tianjin Education Commission for Higher Education(Grant No.2021KJ194)Tianjin Key Medical Discipline(Specialty)Construction Project(Grant No.TJYXZDXK-009A).
文摘Artificial intelligence(AI)is significantly advancing precision medicine,particularly in the fields of immunogenomics,radiomics,and pathomics.In immunogenomics,AI can process vast amounts of genomic and multi-omic data to identify biomarkers associated with immunotherapy responses and disease prognosis,thus providing strong support for personalized treatments.In radiomics,AI can analyze high-dimensional features from computed tomography(CT),magnetic resonance imaging(MRI),and positron emission tomography/computed tomography(PET/CT)images to discover imaging biomarkers associated with tumor heterogeneity,treatment response,and disease progression,thereby enabling non-invasive,real-time assessments for personalized therapy.Pathomics leverages AI for deep analysis of digital pathology images,and can uncover subtle changes in tissue microenvironments,cellular characteristics,and morphological features,and offer unique insights into immunotherapy response prediction and biomarker discovery.These AI-driven technologies not only enhance the speed,accuracy,and robustness of biomarker discovery but also significantly improve the precision,personalization,and effectiveness of clinical treatments,and are driving a shift from empirical to precision medicine.Despite challenges such as data quality,model interpretability,integration of multi-modal data,and privacy protection,the ongoing advancements in AI,coupled with interdisciplinary collaboration,are poised to further enhance AI’s roles in biomarker discovery and immunotherapy response prediction.These improvements are expected to lead to more accurate,personalized treatment strategies and ultimately better patient outcomes,marking a significant step forward in the evolution of precision medicine.
基金the Young Scientists Fund of the National Natural Science Foundation of China(Grant No.82402274 and 82272008)the Science&Technology Development Fund of Tianjin Education Commission for Higher Education(Grant No.2021KJ194).
文摘The automatic classification of thyroid nodules in ultrasound images is a critical research focus in medical imaging.However,publicly available thyroid ultrasound datasets remain scarce.In this study,we developed the Ultrasound Dataset for Thyroid Nodules(UD-TN),a comprehensive dataset containing 10,495 labeled images classified as benign or malignant based on pathology-confirmed results.To establish a benchmark,we proposed the Thyroid Ultrasound Image Neural Network(ThyUNet),a deep learning model designed for accurate nodule classification.By incorporating high-resolution feature enhancement,instance normalization,and dilated convolutions into residual blocks,ThyUNet excels in extracting fine-grained features,particularly for small nodules.Experimental results demonstrate that ThyUNet achieves state-of-the-art performance,with an accuracy of 89.7%,a sensitivity of 0.879,and a specificity of 0.910 on the testing set.These results surpass those of other advanced architectures,highlighting the model’s effectiveness.UD-TN and ThyUNet contribute significantly to advancing intelligent medical diagnostics.Dataset details and access instructions are available at https://github.com/18811755633/Sample-of-UD-TN.
