Computer-Aided Evaluation of Interstitial Lung Diseases
The approach for the diagnosis and treatment of interstitial lung diseases (ILDs) has changed in recent years, mainly for the identification of new entities, such as interstitial lung abnormalities (ILAs) and progressive pulmonary fibrosis (PPF). Clinicians and radiologists are facing new challenges...
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| Format: | Article |
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MDPI AG
2025-04-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/15/7/943 |
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| author | Davide Colombi Maurizio Marvisi Sara Ramponi Laura Balzarini Chiara Mancini Gianluca Milanese Mario Silva Nicola Sverzellati Mario Uccelli Francesco Ferrozzi |
| author_facet | Davide Colombi Maurizio Marvisi Sara Ramponi Laura Balzarini Chiara Mancini Gianluca Milanese Mario Silva Nicola Sverzellati Mario Uccelli Francesco Ferrozzi |
| author_sort | Davide Colombi |
| collection | DOAJ |
| description | The approach for the diagnosis and treatment of interstitial lung diseases (ILDs) has changed in recent years, mainly for the identification of new entities, such as interstitial lung abnormalities (ILAs) and progressive pulmonary fibrosis (PPF). Clinicians and radiologists are facing new challenges for the screening, diagnosis, prognosis, and follow-up of ILDs. The detection and classification of ILAs or the identification of fibrosis progression at high-resolution computed tomography (HRCT) is difficult, with high inter-reader variability, particularly for non-expert radiologists. In the last few years, various software has been developed for ILD evaluation at HRCT, with excellent results, equal to or more reliable than humans. AI tools can classify ILDs, quantify the extent, analyze the features hidden from the human eye, predict prognosis, and evaluate the progression of the disease. More advanced tools can incorporate clinical and radiological data to obtain personalized prognosis, with the potential ability to steer treatment decisions. To step forward and implement in daily practice such tools, more collaboration is required to collect more homogeneous clinical and radiological data; furthermore, more robust, prospective trials, with the new AI-derived biomarkers compared with each other, are needed to demonstrate the real reliability of the computer-aided evaluation of ILDs. |
| format | Article |
| id | doaj-art-2e7517fcaf9b4fe681bc94e9d5c05b48 |
| institution | OA Journals |
| issn | 2075-4418 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-2e7517fcaf9b4fe681bc94e9d5c05b482025-08-20T02:09:13ZengMDPI AGDiagnostics2075-44182025-04-0115794310.3390/diagnostics15070943Computer-Aided Evaluation of Interstitial Lung DiseasesDavide Colombi0Maurizio Marvisi1Sara Ramponi2Laura Balzarini3Chiara Mancini4Gianluca Milanese5Mario Silva6Nicola Sverzellati7Mario Uccelli8Francesco Ferrozzi9Department of Radiology, Istituto Figlie di San Camillo, 26100 Cremona, ItalyDepartment of Internal Medicine and Pneumology, Istituto Figlie di San Camillo, 26100 Cremona, ItalyDepartment of Internal Medicine and Pneumology, Istituto Figlie di San Camillo, 26100 Cremona, ItalyDepartment of Internal Medicine and Pneumology, Istituto Figlie di San Camillo, 26100 Cremona, ItalyDepartment of Internal Medicine and Pneumology, Istituto Figlie di San Camillo, 26100 Cremona, ItalyScienze Radiologiche, Dipartimento di Medicina e Chirurgia, University Hospital of Parma, 43126 Parma, ItalyScienze Radiologiche, Dipartimento di Medicina e Chirurgia, University Hospital of Parma, 43126 Parma, ItalyScienze Radiologiche, Dipartimento di Medicina e Chirurgia, University Hospital of Parma, 43126 Parma, ItalyDepartment of Radiology, Istituto Figlie di San Camillo, 26100 Cremona, ItalyDepartment of Radiology, Istituto Figlie di San Camillo, 26100 Cremona, ItalyThe approach for the diagnosis and treatment of interstitial lung diseases (ILDs) has changed in recent years, mainly for the identification of new entities, such as interstitial lung abnormalities (ILAs) and progressive pulmonary fibrosis (PPF). Clinicians and radiologists are facing new challenges for the screening, diagnosis, prognosis, and follow-up of ILDs. The detection and classification of ILAs or the identification of fibrosis progression at high-resolution computed tomography (HRCT) is difficult, with high inter-reader variability, particularly for non-expert radiologists. In the last few years, various software has been developed for ILD evaluation at HRCT, with excellent results, equal to or more reliable than humans. AI tools can classify ILDs, quantify the extent, analyze the features hidden from the human eye, predict prognosis, and evaluate the progression of the disease. More advanced tools can incorporate clinical and radiological data to obtain personalized prognosis, with the potential ability to steer treatment decisions. To step forward and implement in daily practice such tools, more collaboration is required to collect more homogeneous clinical and radiological data; furthermore, more robust, prospective trials, with the new AI-derived biomarkers compared with each other, are needed to demonstrate the real reliability of the computer-aided evaluation of ILDs.https://www.mdpi.com/2075-4418/15/7/943interstitial lung diseaseAI (artificial intelligence)hierarchical learning |
| spellingShingle | Davide Colombi Maurizio Marvisi Sara Ramponi Laura Balzarini Chiara Mancini Gianluca Milanese Mario Silva Nicola Sverzellati Mario Uccelli Francesco Ferrozzi Computer-Aided Evaluation of Interstitial Lung Diseases Diagnostics interstitial lung disease AI (artificial intelligence) hierarchical learning |
| title | Computer-Aided Evaluation of Interstitial Lung Diseases |
| title_full | Computer-Aided Evaluation of Interstitial Lung Diseases |
| title_fullStr | Computer-Aided Evaluation of Interstitial Lung Diseases |
| title_full_unstemmed | Computer-Aided Evaluation of Interstitial Lung Diseases |
| title_short | Computer-Aided Evaluation of Interstitial Lung Diseases |
| title_sort | computer aided evaluation of interstitial lung diseases |
| topic | interstitial lung disease AI (artificial intelligence) hierarchical learning |
| url | https://www.mdpi.com/2075-4418/15/7/943 |
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