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April 30, 2026<\/p>\n
2 min read<\/p>\n<\/p><\/div>\n
\n \n \n Add topic to email alerts \n <\/span> \n <\/p>\n\n\n \n Receive an email when new articles are posted on \n <\/p>\n\n Please provide your email address to receive an email when new articles are posted on .<\/p><\/div>\n<\/p><\/div>\n “ \n data-action=”subscribe”> \n Subscribe \n <\/button>\n <\/div>\n<\/p><\/div>\n We were unable to process your request. Please try again later. If you continue to have this issue please contact customerservice@slackinc.com.<\/strong> <\/p>\nBack to Healio<\/button><\/p>\n<\/div>\n<\/div><\/div>\n<\/p><\/div>\n<\/p><\/div>\n<\/div>\nKey takeaways:<\/h2>\n\nMachine learning was used to detect interstitial lung disease in patients with connective tissue disease.<\/li>\nThe data may enhance interpretation by physicians or radiologists. .<\/li>\n<\/ul>\nResearchers successfully used machine learning on lung ultrasound images to detect interstitial lung disease in patients with systemic sclerosis and idiopathic inflammatory myopathy, according to data published in Arthritis Care & Research.<\/i><\/p>\n\u201cBeyond its accuracy, [lung ultrasound (LUS)] offers advantages such as low cost, accessibility, no radiation, and sustainability,\u201d Robert M. Fairchild, MD, PhD, <\/b>clinical chief of the division of immunology and rheumatology at Stanford University, and colleagues wrote. \u201cHowever, operator dependence and variability in acquiring and interpreting LUS remain a potential drawback. This may explain limited adoption of LUS for [connective tissue disease (CTD)]-ILD screening despite evidence supporting its use.<\/p>\n \n \n \n \n <\/source><\/source><\/source><\/source><\/source><\/picture> \n Data derived from Fairchild RM, et al. Arthritis Care Res<\/i>. 2026;doi:10.1002\/acr.80054. \n <\/figcaption> \n <\/figure>\n\u201cIn this context, applying artificial intelligence (AI) to LUS in ILD offers an opportunity to address two key issues,\u201d they added. \u201cFirst, AI can help independently validate the diagnostic sufficiency of pleural-based sonographic features in ILD. … Second, AI has the potential to reduce operator-dependent variability, a major barrier to clinical adoption, thereby supporting broader implementation of LUS as a scalable, accessible, and reliable modality for ILD detection in connective tissue diseases.\u201d<\/p>\nIn the current study, Fairchild and colleagues aimed to determine whether the application of deep learning, convolutional neural networks (CNN) to lung ultrasound could accurately detect ILD and its severity among patients with SSc or idiopathic inflammatory myopathy. The analysis included data for 3,920 ultrasound images from 140 individuals with SSc or idiopathic inflammatory myopathy plus ILD. Eligible participants had undergone paired lung ultrasound and chest CT. <\/p>\nThe researchers used transfer learning to \u201cfine tune\u201d three pre-trained CNN architectures \u2014 InceptionV3, ResNet-50 and VGG-16 \u2014 and develop a de novo lightweight architecture called LUS-Net, they wrote. In all, 74 patients were assigned to a development test set while 66 were assigned to an independent test set.<\/p>\nAccording to the researchers, VGG-16 performed best in assessing patient-level data, with a sensitivity of 97.4% and a specificity of 92.6% (AUC = 0.972). VGG-16 also correlated strongly with pulmonary function test data and CT severity, according to the findings. <\/p>\n\u201cGrad-CAM highlighted pleural features as the primary regions influencing model predictions,\u201d Fairchild told Healio in an interview. \u201cCNN performance matched or exceeded LUS-ILD-24 interpretation \u2014 a high-performing and validated human lung ultrasound interpretation criteria for ILD detection in systemic sclerosis and inflammatory myopathy.\u201d<\/p>\nThe researchers concluded that deep learning may successfully be applied to lung ultrasound for the detection of ILD. These data may enhance interpretation by physicians or radiologists, they added.<\/p>\n\u201cDeep learning-based interpretation of lung ultrasound provides a promising and scalable solution for ILD detection in connective tissue disease,\u201d Fairchild and colleagues wrote. \u201cBy combining the portability and safety of LUS with the power of CNNs, this approach has the potential to standardize interpretation, enhance understanding and transparency with explainable AI, and expand access to high-quality ILD screening and monitoring across a broad range of clinical settings.\u201d<\/p>\nDeep learning AI may successfully be applied to lung ultrasound for the detection of ILD in SSc and inflammatory myopathy.<\/p>\nVGG-16 performed best in assessing patient-level data, with a sensitivity of 97.4% and a specificity of 92.6%.<\/p>\nAI could standardize interpretation, enhance understanding and transparency, and expand access to ILD screening, they wrote.<\/p>\n
\n Receive an email when new articles are posted on \n <\/p>\n\n Please provide your email address to receive an email when new articles are posted on .<\/p><\/div>\n<\/p><\/div>\n “ \n data-action=”subscribe”> \n Subscribe \n <\/button>\n <\/div>\n<\/p><\/div>\n We were unable to process your request. Please try again later. If you continue to have this issue please contact customerservice@slackinc.com.<\/strong> <\/p>\nBack to Healio<\/button><\/p>\n<\/div>\n<\/div><\/div>\n<\/p><\/div>\n<\/p><\/div>\n<\/div>\nKey takeaways:<\/h2>\n\nMachine learning was used to detect interstitial lung disease in patients with connective tissue disease.<\/li>\nThe data may enhance interpretation by physicians or radiologists. .<\/li>\n<\/ul>\nResearchers successfully used machine learning on lung ultrasound images to detect interstitial lung disease in patients with systemic sclerosis and idiopathic inflammatory myopathy, according to data published in Arthritis Care & Research.<\/i><\/p>\n\u201cBeyond its accuracy, [lung ultrasound (LUS)] offers advantages such as low cost, accessibility, no radiation, and sustainability,\u201d Robert M. Fairchild, MD, PhD, <\/b>clinical chief of the division of immunology and rheumatology at Stanford University, and colleagues wrote. \u201cHowever, operator dependence and variability in acquiring and interpreting LUS remain a potential drawback. This may explain limited adoption of LUS for [connective tissue disease (CTD)]-ILD screening despite evidence supporting its use.<\/p>\n \n \n \n \n <\/source><\/source><\/source><\/source><\/source><\/picture> \n Data derived from Fairchild RM, et al. Arthritis Care Res<\/i>. 2026;doi:10.1002\/acr.80054. \n <\/figcaption> \n <\/figure>\n\u201cIn this context, applying artificial intelligence (AI) to LUS in ILD offers an opportunity to address two key issues,\u201d they added. \u201cFirst, AI can help independently validate the diagnostic sufficiency of pleural-based sonographic features in ILD. … Second, AI has the potential to reduce operator-dependent variability, a major barrier to clinical adoption, thereby supporting broader implementation of LUS as a scalable, accessible, and reliable modality for ILD detection in connective tissue diseases.\u201d<\/p>\nIn the current study, Fairchild and colleagues aimed to determine whether the application of deep learning, convolutional neural networks (CNN) to lung ultrasound could accurately detect ILD and its severity among patients with SSc or idiopathic inflammatory myopathy. The analysis included data for 3,920 ultrasound images from 140 individuals with SSc or idiopathic inflammatory myopathy plus ILD. Eligible participants had undergone paired lung ultrasound and chest CT. <\/p>\nThe researchers used transfer learning to \u201cfine tune\u201d three pre-trained CNN architectures \u2014 InceptionV3, ResNet-50 and VGG-16 \u2014 and develop a de novo lightweight architecture called LUS-Net, they wrote. In all, 74 patients were assigned to a development test set while 66 were assigned to an independent test set.<\/p>\nAccording to the researchers, VGG-16 performed best in assessing patient-level data, with a sensitivity of 97.4% and a specificity of 92.6% (AUC = 0.972). VGG-16 also correlated strongly with pulmonary function test data and CT severity, according to the findings. <\/p>\n\u201cGrad-CAM highlighted pleural features as the primary regions influencing model predictions,\u201d Fairchild told Healio in an interview. \u201cCNN performance matched or exceeded LUS-ILD-24 interpretation \u2014 a high-performing and validated human lung ultrasound interpretation criteria for ILD detection in systemic sclerosis and inflammatory myopathy.\u201d<\/p>\nThe researchers concluded that deep learning may successfully be applied to lung ultrasound for the detection of ILD. These data may enhance interpretation by physicians or radiologists, they added.<\/p>\n\u201cDeep learning-based interpretation of lung ultrasound provides a promising and scalable solution for ILD detection in connective tissue disease,\u201d Fairchild and colleagues wrote. \u201cBy combining the portability and safety of LUS with the power of CNNs, this approach has the potential to standardize interpretation, enhance understanding and transparency with explainable AI, and expand access to high-quality ILD screening and monitoring across a broad range of clinical settings.\u201d<\/p>\nDeep learning AI may successfully be applied to lung ultrasound for the detection of ILD in SSc and inflammatory myopathy.<\/p>\nVGG-16 performed best in assessing patient-level data, with a sensitivity of 97.4% and a specificity of 92.6%.<\/p>\nAI could standardize interpretation, enhance understanding and transparency, and expand access to ILD screening, they wrote.<\/p>\n
“ \n data-action=”subscribe”> \n Subscribe \n <\/button>\n <\/div>\n<\/p><\/div>\n We were unable to process your request. Please try again later. If you continue to have this issue please contact customerservice@slackinc.com.<\/strong> <\/p>\nBack to Healio<\/button><\/p>\n<\/div>\n<\/div><\/div>\n<\/p><\/div>\n<\/p><\/div>\n<\/div>\nKey takeaways:<\/h2>\n\nMachine learning was used to detect interstitial lung disease in patients with connective tissue disease.<\/li>\nThe data may enhance interpretation by physicians or radiologists. .<\/li>\n<\/ul>\nResearchers successfully used machine learning on lung ultrasound images to detect interstitial lung disease in patients with systemic sclerosis and idiopathic inflammatory myopathy, according to data published in Arthritis Care & Research.<\/i><\/p>\n\u201cBeyond its accuracy, [lung ultrasound (LUS)] offers advantages such as low cost, accessibility, no radiation, and sustainability,\u201d Robert M. Fairchild, MD, PhD, <\/b>clinical chief of the division of immunology and rheumatology at Stanford University, and colleagues wrote. \u201cHowever, operator dependence and variability in acquiring and interpreting LUS remain a potential drawback. This may explain limited adoption of LUS for [connective tissue disease (CTD)]-ILD screening despite evidence supporting its use.<\/p>\n \n \n \n \n <\/source><\/source><\/source><\/source><\/source><\/picture> \n Data derived from Fairchild RM, et al. Arthritis Care Res<\/i>. 2026;doi:10.1002\/acr.80054. \n <\/figcaption> \n <\/figure>\n\u201cIn this context, applying artificial intelligence (AI) to LUS in ILD offers an opportunity to address two key issues,\u201d they added. \u201cFirst, AI can help independently validate the diagnostic sufficiency of pleural-based sonographic features in ILD. … Second, AI has the potential to reduce operator-dependent variability, a major barrier to clinical adoption, thereby supporting broader implementation of LUS as a scalable, accessible, and reliable modality for ILD detection in connective tissue diseases.\u201d<\/p>\nIn the current study, Fairchild and colleagues aimed to determine whether the application of deep learning, convolutional neural networks (CNN) to lung ultrasound could accurately detect ILD and its severity among patients with SSc or idiopathic inflammatory myopathy. The analysis included data for 3,920 ultrasound images from 140 individuals with SSc or idiopathic inflammatory myopathy plus ILD. Eligible participants had undergone paired lung ultrasound and chest CT. <\/p>\nThe researchers used transfer learning to \u201cfine tune\u201d three pre-trained CNN architectures \u2014 InceptionV3, ResNet-50 and VGG-16 \u2014 and develop a de novo lightweight architecture called LUS-Net, they wrote. In all, 74 patients were assigned to a development test set while 66 were assigned to an independent test set.<\/p>\nAccording to the researchers, VGG-16 performed best in assessing patient-level data, with a sensitivity of 97.4% and a specificity of 92.6% (AUC = 0.972). VGG-16 also correlated strongly with pulmonary function test data and CT severity, according to the findings. <\/p>\n\u201cGrad-CAM highlighted pleural features as the primary regions influencing model predictions,\u201d Fairchild told Healio in an interview. \u201cCNN performance matched or exceeded LUS-ILD-24 interpretation \u2014 a high-performing and validated human lung ultrasound interpretation criteria for ILD detection in systemic sclerosis and inflammatory myopathy.\u201d<\/p>\nThe researchers concluded that deep learning may successfully be applied to lung ultrasound for the detection of ILD. These data may enhance interpretation by physicians or radiologists, they added.<\/p>\n\u201cDeep learning-based interpretation of lung ultrasound provides a promising and scalable solution for ILD detection in connective tissue disease,\u201d Fairchild and colleagues wrote. \u201cBy combining the portability and safety of LUS with the power of CNNs, this approach has the potential to standardize interpretation, enhance understanding and transparency with explainable AI, and expand access to high-quality ILD screening and monitoring across a broad range of clinical settings.\u201d<\/p>\nDeep learning AI may successfully be applied to lung ultrasound for the detection of ILD in SSc and inflammatory myopathy.<\/p>\nVGG-16 performed best in assessing patient-level data, with a sensitivity of 97.4% and a specificity of 92.6%.<\/p>\nAI could standardize interpretation, enhance understanding and transparency, and expand access to ILD screening, they wrote.<\/p>\n
Back to Healio<\/button><\/p>\n<\/div>\n<\/div><\/div>\n<\/p><\/div>\n<\/p><\/div>\n<\/div>\nKey takeaways:<\/h2>\n\nMachine learning was used to detect interstitial lung disease in patients with connective tissue disease.<\/li>\nThe data may enhance interpretation by physicians or radiologists. .<\/li>\n<\/ul>\nResearchers successfully used machine learning on lung ultrasound images to detect interstitial lung disease in patients with systemic sclerosis and idiopathic inflammatory myopathy, according to data published in Arthritis Care & Research.<\/i><\/p>\n\u201cBeyond its accuracy, [lung ultrasound (LUS)] offers advantages such as low cost, accessibility, no radiation, and sustainability,\u201d Robert M. Fairchild, MD, PhD, <\/b>clinical chief of the division of immunology and rheumatology at Stanford University, and colleagues wrote. \u201cHowever, operator dependence and variability in acquiring and interpreting LUS remain a potential drawback. This may explain limited adoption of LUS for [connective tissue disease (CTD)]-ILD screening despite evidence supporting its use.<\/p>\n \n \n \n \n <\/source><\/source><\/source><\/source><\/source><\/picture> \n Data derived from Fairchild RM, et al. Arthritis Care Res<\/i>. 2026;doi:10.1002\/acr.80054. \n <\/figcaption> \n <\/figure>\n\u201cIn this context, applying artificial intelligence (AI) to LUS in ILD offers an opportunity to address two key issues,\u201d they added. \u201cFirst, AI can help independently validate the diagnostic sufficiency of pleural-based sonographic features in ILD. … Second, AI has the potential to reduce operator-dependent variability, a major barrier to clinical adoption, thereby supporting broader implementation of LUS as a scalable, accessible, and reliable modality for ILD detection in connective tissue diseases.\u201d<\/p>\nIn the current study, Fairchild and colleagues aimed to determine whether the application of deep learning, convolutional neural networks (CNN) to lung ultrasound could accurately detect ILD and its severity among patients with SSc or idiopathic inflammatory myopathy. The analysis included data for 3,920 ultrasound images from 140 individuals with SSc or idiopathic inflammatory myopathy plus ILD. Eligible participants had undergone paired lung ultrasound and chest CT. <\/p>\nThe researchers used transfer learning to \u201cfine tune\u201d three pre-trained CNN architectures \u2014 InceptionV3, ResNet-50 and VGG-16 \u2014 and develop a de novo lightweight architecture called LUS-Net, they wrote. In all, 74 patients were assigned to a development test set while 66 were assigned to an independent test set.<\/p>\nAccording to the researchers, VGG-16 performed best in assessing patient-level data, with a sensitivity of 97.4% and a specificity of 92.6% (AUC = 0.972). VGG-16 also correlated strongly with pulmonary function test data and CT severity, according to the findings. <\/p>\n\u201cGrad-CAM highlighted pleural features as the primary regions influencing model predictions,\u201d Fairchild told Healio in an interview. \u201cCNN performance matched or exceeded LUS-ILD-24 interpretation \u2014 a high-performing and validated human lung ultrasound interpretation criteria for ILD detection in systemic sclerosis and inflammatory myopathy.\u201d<\/p>\nThe researchers concluded that deep learning may successfully be applied to lung ultrasound for the detection of ILD. These data may enhance interpretation by physicians or radiologists, they added.<\/p>\n\u201cDeep learning-based interpretation of lung ultrasound provides a promising and scalable solution for ILD detection in connective tissue disease,\u201d Fairchild and colleagues wrote. \u201cBy combining the portability and safety of LUS with the power of CNNs, this approach has the potential to standardize interpretation, enhance understanding and transparency with explainable AI, and expand access to high-quality ILD screening and monitoring across a broad range of clinical settings.\u201d<\/p>\nDeep learning AI may successfully be applied to lung ultrasound for the detection of ILD in SSc and inflammatory myopathy.<\/p>\nVGG-16 performed best in assessing patient-level data, with a sensitivity of 97.4% and a specificity of 92.6%.<\/p>\nAI could standardize interpretation, enhance understanding and transparency, and expand access to ILD screening, they wrote.<\/p>\n
Researchers successfully used machine learning on lung ultrasound images to detect interstitial lung disease in patients with systemic sclerosis and idiopathic inflammatory myopathy, according to data published in Arthritis Care & Research.<\/i><\/p>\n
\u201cBeyond its accuracy, [lung ultrasound (LUS)] offers advantages such as low cost, accessibility, no radiation, and sustainability,\u201d Robert M. Fairchild, MD, PhD, <\/b>clinical chief of the division of immunology and rheumatology at Stanford University, and colleagues wrote. \u201cHowever, operator dependence and variability in acquiring and interpreting LUS remain a potential drawback. This may explain limited adoption of LUS for [connective tissue disease (CTD)]-ILD screening despite evidence supporting its use.<\/p>\n \n \n \n \n <\/source><\/source><\/source><\/source><\/source><\/picture> \n Data derived from Fairchild RM, et al. Arthritis Care Res<\/i>. 2026;doi:10.1002\/acr.80054. \n <\/figcaption> \n <\/figure>\n\u201cIn this context, applying artificial intelligence (AI) to LUS in ILD offers an opportunity to address two key issues,\u201d they added. \u201cFirst, AI can help independently validate the diagnostic sufficiency of pleural-based sonographic features in ILD. … Second, AI has the potential to reduce operator-dependent variability, a major barrier to clinical adoption, thereby supporting broader implementation of LUS as a scalable, accessible, and reliable modality for ILD detection in connective tissue diseases.\u201d<\/p>\nIn the current study, Fairchild and colleagues aimed to determine whether the application of deep learning, convolutional neural networks (CNN) to lung ultrasound could accurately detect ILD and its severity among patients with SSc or idiopathic inflammatory myopathy. The analysis included data for 3,920 ultrasound images from 140 individuals with SSc or idiopathic inflammatory myopathy plus ILD. Eligible participants had undergone paired lung ultrasound and chest CT. <\/p>\nThe researchers used transfer learning to \u201cfine tune\u201d three pre-trained CNN architectures \u2014 InceptionV3, ResNet-50 and VGG-16 \u2014 and develop a de novo lightweight architecture called LUS-Net, they wrote. In all, 74 patients were assigned to a development test set while 66 were assigned to an independent test set.<\/p>\nAccording to the researchers, VGG-16 performed best in assessing patient-level data, with a sensitivity of 97.4% and a specificity of 92.6% (AUC = 0.972). VGG-16 also correlated strongly with pulmonary function test data and CT severity, according to the findings. <\/p>\n\u201cGrad-CAM highlighted pleural features as the primary regions influencing model predictions,\u201d Fairchild told Healio in an interview. \u201cCNN performance matched or exceeded LUS-ILD-24 interpretation \u2014 a high-performing and validated human lung ultrasound interpretation criteria for ILD detection in systemic sclerosis and inflammatory myopathy.\u201d<\/p>\nThe researchers concluded that deep learning may successfully be applied to lung ultrasound for the detection of ILD. These data may enhance interpretation by physicians or radiologists, they added.<\/p>\n\u201cDeep learning-based interpretation of lung ultrasound provides a promising and scalable solution for ILD detection in connective tissue disease,\u201d Fairchild and colleagues wrote. \u201cBy combining the portability and safety of LUS with the power of CNNs, this approach has the potential to standardize interpretation, enhance understanding and transparency with explainable AI, and expand access to high-quality ILD screening and monitoring across a broad range of clinical settings.\u201d<\/p>\nDeep learning AI may successfully be applied to lung ultrasound for the detection of ILD in SSc and inflammatory myopathy.<\/p>\nVGG-16 performed best in assessing patient-level data, with a sensitivity of 97.4% and a specificity of 92.6%.<\/p>\nAI could standardize interpretation, enhance understanding and transparency, and expand access to ILD screening, they wrote.<\/p>\n
\u201cIn this context, applying artificial intelligence (AI) to LUS in ILD offers an opportunity to address two key issues,\u201d they added. \u201cFirst, AI can help independently validate the diagnostic sufficiency of pleural-based sonographic features in ILD. … Second, AI has the potential to reduce operator-dependent variability, a major barrier to clinical adoption, thereby supporting broader implementation of LUS as a scalable, accessible, and reliable modality for ILD detection in connective tissue diseases.\u201d<\/p>\n
In the current study, Fairchild and colleagues aimed to determine whether the application of deep learning, convolutional neural networks (CNN) to lung ultrasound could accurately detect ILD and its severity among patients with SSc or idiopathic inflammatory myopathy. The analysis included data for 3,920 ultrasound images from 140 individuals with SSc or idiopathic inflammatory myopathy plus ILD. Eligible participants had undergone paired lung ultrasound and chest CT. <\/p>\n
The researchers used transfer learning to \u201cfine tune\u201d three pre-trained CNN architectures \u2014 InceptionV3, ResNet-50 and VGG-16 \u2014 and develop a de novo lightweight architecture called LUS-Net, they wrote. In all, 74 patients were assigned to a development test set while 66 were assigned to an independent test set.<\/p>\n
According to the researchers, VGG-16 performed best in assessing patient-level data, with a sensitivity of 97.4% and a specificity of 92.6% (AUC = 0.972). VGG-16 also correlated strongly with pulmonary function test data and CT severity, according to the findings. <\/p>\n
\u201cGrad-CAM highlighted pleural features as the primary regions influencing model predictions,\u201d Fairchild told Healio in an interview. \u201cCNN performance matched or exceeded LUS-ILD-24 interpretation \u2014 a high-performing and validated human lung ultrasound interpretation criteria for ILD detection in systemic sclerosis and inflammatory myopathy.\u201d<\/p>\n
The researchers concluded that deep learning may successfully be applied to lung ultrasound for the detection of ILD. These data may enhance interpretation by physicians or radiologists, they added.<\/p>\n
\u201cDeep learning-based interpretation of lung ultrasound provides a promising and scalable solution for ILD detection in connective tissue disease,\u201d Fairchild and colleagues wrote. \u201cBy combining the portability and safety of LUS with the power of CNNs, this approach has the potential to standardize interpretation, enhance understanding and transparency with explainable AI, and expand access to high-quality ILD screening and monitoring across a broad range of clinical settings.\u201d<\/p>\n
Deep learning AI may successfully be applied to lung ultrasound for the detection of ILD in SSc and inflammatory myopathy.<\/p>\n
VGG-16 performed best in assessing patient-level data, with a sensitivity of 97.4% and a specificity of 92.6%.<\/p>\n
AI could standardize interpretation, enhance understanding and transparency, and expand access to ILD screening, they wrote.<\/p>\n
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