A NEW ARTIFICIAL INTELLIGENCE-FOCUSED PARADIGM IN OCCUPATIONAL DISEASES: DIGITAL TRANSFORMATION IN PNEUMOCONIOSIS DIAGNOSIS AND MONITORING

Öz

Pneumoconiosis is a major occupational lung disease characterized by progressive fibrosis and respiratory failure, particularly affecting workers in heavy industry such as mining, stone cutting, foundry work, and asbestos-related sectors. Conventional diagnostic and follow-up strategies rely largely on radiological assessment, clinical findings, and exposure history; however, the subjective nature of visual interpretation, the limited ability to detect early-stage changes, and considerable inter-reader variability highlight the need for more objective and standardized digital approaches. This narrative review aims to provide a comprehensive overview of the role of contemporary digital technologies in the diagnosis and monitoring of pneumoconiosis, including artificial intelligence (AI)-based imaging, radiomics, quantitative computed tomography (qCT), automated ILO classification systems, telemonitoring platforms, mobile health (mHealth) applications, wearable sensors, and biomarker-based digital follow-up. Current evidence indicates that deep learning and radiomic methods substantially enhance early detection and quantitative characterization of pneumoconiotic lesions, while qCT-derived metrics correlate strongly with disease progression. In parallel, telemonitoring, mHealth solutions, wearable devices, and the integration of biomarkers such as KL-6, SP-D, and TGF-β into digital platforms enable more continuous, multidimensional, and personalized monitoring of disease activity. Nevertheless, challenges related to data privacy, lack of standardization, the need for multicenter external validation of algorithms, and inequalities in access to digital infrastructure remain significant barriers. In conclusion, the structured integration of digital health technologies into pneumoconiosis management offers a novel paradigm that supports earlier diagnosis, continuous monitoring, and individualized clinical decision-making.

Anahtar Kelimeler

• Pneumoconiosis
• Diagnosis
• Follow-up
• Artificial intelligence
• Digital health

MESLEK HASTALIKLARINDA YAPAY ZEKÂ ODAKLI YENİ PARADİGMA: PNÖMOKONYOZ TANISI VE TAKİBİNDE DİJİTAL DÖNÜŞÜM

Öz

Pnömokonyoz, ağır sanayi kollarında çalışan bireylerde görülen, ilerleyici fibrozis ve solunum yetmezliği ile seyreden önemli bir meslek hastalığıdır. Geleneksel tanı ve takip yaklaşımları büyük ölçüde radyolojik değerlendirme, klinik bulgular ve mesleki öyküye dayansa da, bu yöntemlerin subjektif oluşu, erken evre değişikliklerin yakalanmasındaki yetersizlik ve okuyucular arası farklılıklar, daha nesnel ve standardize dijital sistemlere olan gereksinimi artırmaktadır. Bu derlemenin amacı, pnömokonyozun tanı ve izlem süreçlerinde yapay zekâ temelli görüntüleme sistemleri, radyomik analizler, kantitatif BT (qCT), otomatik ILO sınıflandırma, tele-izlem, mobil sağlık uygulamaları, giyilebilir sensörler ve biyobelirteç tabanlı yaklaşımlar gibi güncel dijital teknolojilerin rolünü bütüncül bir bakış açısıyla değerlendirmektir. Literatür, derin öğrenme ve radyomik yöntemlerinin pnömokonyozun erken evrede saptanmasına ve lezyonların kantitatif olarak karakterize edilmesine önemli katkı sağladığını göstermektedir. Benzer şekilde tele-izlem, m-sağlık çözümleri, giyilebilir sensörler ve KL-6, SP-D, TGF-β gibi biyobelirteçlerin dijital takibe entegrasyonu, hastalığın dinamik doğasının daha yakından izlenmesine ve kişiselleştirilmiş izlem stratejilerinin geliştirilmesine imkân tanımaktadır. Bununla birlikte veri güvenliği, standardizasyon eksiklikleri, algoritmaların çok merkezli doğrulama gereksinimi ve teknolojik erişim eşitsizlikleri, dijital dönüşümün temel sınırlılıkları olarak öne çıkmaktadır. Sonuç olarak, pnömokonyoz yönetiminde dijital sağlık teknolojilerinin uygun şekilde entegrasyonu, erken tanı, sürekli takip ve bireye özgü klinik karar desteğini mümkün kılan yeni bir paradigma sunmaktadır.

Anahtar Kelimeler

• Pnömokonyoz
• Tanı
• Takip
• Yapay zekâ
• Dijital sağlık

Kaynakça

1. Beer, C., Kolstad, H. A., Søndergaard, K., Bendstrup, E., Heederik, D., Olsen, K. E., et al. (2017). A systematic review of occupational exposure to coal dust and the risk of interstitial lung diseases. European Clinical Respiratory Journal, 4(1), 1264711.
2. Vanka, K. S., Shukla, S., Gomez, H. M., James, C., Palanisami, T., Williams, K., et al. (2022). Understanding the pathogenesis of occupational coal and silica dust-associated lung disease. European Respiratory Review, 31(165), 210250.
3. Hayashi, H., Ashizawa, K., Takahashi, M., Kato, K., Arakawa, H., Kishimoto, T., et al. (2022). The diagnosis of early pneumoconiosis in dust-exposed workers: Comparison of chest radiography and computed tomography. Acta Radiologica, 63(7), 909–913.
4. Gezer, N. S. (2024). İnterstisyel akciğer hastalığı, kantitatif BT analizi ve yapay zekâ uygulamaları. Türk Radyoloji Seminerleri, 12(1), 162–176.
5. Wijsenbeek, M. S., Moor, C. C., Johannson, K. A., Jackson, P. D., Khor, Y. H., Kondoh, Y., et al. (2023). Home monitoring in interstitial lung diseases. The Lancet Respiratory Medicine, 11(1), 97–110.
6. Hu, X., Liu, X. D., Gao, T., & Guo, Q. (2025). The application value of HRCT quantitative analysis in early pneumoconiosis and concomitant diseases. Medicine, 104(18), e42374.
7. Ma, Y., Cui, F., Yao, Y., Shen, F., Qin, H., Li, B., et al. (2025). Machine learning models for the prediction of preclinical coal workers’ pneumoconiosis: integrating CT radiomics and occupational health surveillance records. Journal of Translational Medicine, 23(1), 896.
8. Wei, Q., Song, W., Fu, L., Lei, Y., & Wang, Q. (2025). Digital radiography-based pneumoconiosis diagnosis via Vision Transformer networks. Journal on Artificial Intelligence, 7(1), 39–53.

9. Li, X., Li, Q., Xie, X., Wang, W., Li, X., Zhang, T., et al. (2025). Integrating CT radiomics and clinical data with machine learning to predict fibrosis progression in coalworker pneumoconiosis. Frontiers in Medicine, 12, 1599739.
10. Devnath, L., Luo, S., Summons, P., Wang, D., Shaukat, K., Hameed, I. A., et al. (2022). Deep ensemble learning for the automatic detection of pneumoconiosis in coal workers’ chest X-ray radiography. Journal of Clinical Medicine, 11(18), 5342.
11. Dong, H., Zhu, B., Kong, X., Su, X., Liu, T., & Zhang, X. (2025). Deep learning-based algorithm for classifying high-resolution computed tomography features in coal workers’ pneumoconiosis. BioMedical Engineering OnLine, 24, 7.
12. Akira, M., & Suganuma, N. (2023). Imaging diagnosis of pneumoconiosis with predominant nodular pattern: HRCT and pathologic findings. Clinical Imaging, 97, 28–33.
13. Zhang, Z., Ren, J., Tao, X., Tang, W., Zhao, S., Zhou, L., et al. (2021). Automatic segmentation of pulmonary lobes on low-dose computed tomography using deep learning. Annals of Translational Medicine, 9(4), 291.
14. Zhou, Z., Li, X., Ji, H., Xu, X., Chang, Z., Wu, K., et al. (2024). Application of improved Unet network in the recognition and segmentation of lung CT images in patients with pneumoconiosis. BMC Medical Imaging, 24, 220.
15. Cai, G.-W., Liu, Y.-B., Feng, Q.-J., Liang, R.-H., Zeng, Q.-S., Deng, Y., et al. (2023). Semi-supervised segmentation of interstitial lung disease patterns from CT images via self-training with selective re-training. Bioengineering, 10(7), 830.
16. Mayerhoefer, M. E., Materka, A., Langs, G., Häggström, I., Szczypiński, P., Gibbs, P., et al. (2020). Introduction to radiomics. Journal of Nuclear Medicine, 61(4), 488–495.
17. Wang, Z., Hu, M., Zeng, M., & Wang, G. (2022). Intelligent image diagnosis of pneumoconiosis based on wavelet transform-derived texture features. Computational and Mathematical Methods in Medicine, 2022, Article 2037019.
18. Haga, A., Iwasawa, T., Misumi, T., Okudela, K., Oda, T., Kitamura, H., et al. (2024). Correlation of CT-based radiomics analysis with pathological cellular infiltration in fibrosing interstitial lung diseases. Japanese Journal of Radiology, 42(10), 1157–1167.
19. Colligiani, L., Marzi, C., Uggenti, V., Colantonio, S., Tavanti, L., Pistelli, F., et al. (2025). Unlocking the potential of radiomics in identifying fibrosing and inflammatory patterns in interstitial lung disease. La Radiologia Medica, 130, 1797–1807.
20. Shin, B., Oh, Y. J., Kim, J., Park, S. G., Lee, K. S., & Lee, H. Y. (2024). Correlation between CT-based phenotypes and serum biomarker in interstitial lung diseases. BMC Pulmonary Medicine, 24, 523.
21. Dixon, G., Thould, H., Wells, M., Tsaneva-Atanasova, K., Scotton, C. J., Gibbons, M. A., et al. (2025). A systematic review of the role of quantitative CT in the prognostication and disease monitoring of interstitial lung disease. European Respiratory Review, 34(176), Article 240194.
22. Bartholmai, B. J., Raghunath, S., Karwoski, R. A., Moua, T., Rajagopalan, S., Maldonado, F., et al. (2013). Quantitative computed tomography imaging of interstitial lung diseases. Journal of Thoracic Imaging, 28(5), 298–307.
23. Pyo, J., Chau, N.-K., Park, E.-K., & Choi, S. (2023). Computed tomography-based imaging biomarker identifies coal workers’ pneumoconiosis. Frontiers in Physiology, 14, 1288246.
24. Chau, N.-K., Park, E.-K., & Choi, S. (2025). Longitudinal analysis of coal workers’ pneumoconiosis using enhanced-resolution computed tomography images: Unveiling patterns in lung structure, function, and clinical correlations. Frontiers in Physiology, 16, 1578058.
25. Blackley, D. J., Halldin, C. N., Petsonk, E. L., & Laney, A. S. (2017). Pneumoconioses radiographs in a large population of U.S. coal workers: Variability in A Reader and B Reader classifications by using the International Labour Office Classification. Radiology, 284(3), 870–876.
26. Zhang, L., Rong, R., Li, Q., Yang, D. M., Yao, B., Luo, D., et al. (2021). A deep learning-based model for screening and staging pneumoconiosis. Scientific Reports, 11, 2201.
27. Zhang, Y., Zheng, B., Zeng, F., Cheng, X., Wu, T., Peng, Y., et al. (2024). Potential of digital chest radiography-based deep learning in screening and diagnosing pneumoconiosis: An observational study. Medicine, 103(25), e38478.
28. Shin, B., Oh, Y. J., Kim, J., Park, S. G., Lee, K. S., & Lee, H. Y. (2024). Correlation between CT-based phenotypes and serum biomarker in interstitial lung diseases. BMC Pulmonary Medicine, 24, Article 523.
29. Chen, Y., Zhang, H., Li, X., Wang, J., Liu, S., & Zhou, Q. (2025). Developing and validation of a multimodal-based machine learning model for diagnosis of usual interstitial pneumonia. Frontiers in Medicine, 12, 1154. Dong, H., Zhu, B., Kong, X., & Zhang, X. (2023). Efficient clinical data analysis for prediction of coal workers’ pneumoconiosis using machine learning algorithms. Occupational and Environmental Medicine, 80(3), 170–176. Naqvi, M., Borton, R., Lines, S., Dallas, J., Mandizha, J., Almond, H., et al. (2025). Home monitoring in interstitial lung disease: Protocol for a real-world observational study. JMIR Research Protocols, 14, e65339.
30. Barth, S., Edwards, C., Saini, G., Haider, Y., Williams, N. P., Storrar, W., et al. (2024). Feasibility and acceptability of remotely monitoring spirometry and pulse oximetry as part of interstitial lung disease clinical care: A single-arm observational study. Respiratory Research, 25, Article 162.
31. Althobiani, M. A., Evans, R. A., Alqahtani, J. S., Aldhahir, A. M., Russell, A. M., Hurst, J. R., et al. (2021). Home monitoring of physiology and symptoms to detect interstitial lung disease exacerbations and progression: A systematic review. ERJ Open Research, 7(4), Article 00441-2021.
32. Sapouna, V., Kitixis, P., Petrou, E., Michailidou, T., Dalamarinis, P., & Kortianou, E. (2023). Mobile health applications designed for self-management of chronic pulmonary diseases in children and adolescents: A systematic mapping review. Jornal Brasileiro de Pneumologia, 49(5), e20230201.
33. Salim, H., Cheong, A. T., Sharif-Ghazali, S., Lee, P. Y., Lim, P. Y., Khoo, E. M., et al. (2023). A self-management app to improve asthma control in adults with limited health literacy: A mixed-method feasibility study. BMC Medical Informatics and Decision Making, 23, Article 194.
34. Moon, K. S., & Lee, S. Q. (2023). A wearable multimodal wireless sensing system for respiratory monitoring and analysis. Sensors, 23(15), 6790.
35. Rutkowski, S., Buekers, J., Rutkowska, A., Cieślik, B., & Szczegielniak, J. (2021). Monitoring physical activity with a wearable sensor in patients with COPD during in-hospital pulmonary rehabilitation program: A pilot study. Sensors, 21(8), 2742.
36. Xue, C., Wu, N., Li, X., Qiu, M., Du, X., Ye, Q., et al. (2017). Serum concentrations of Krebs von den Lungen-6, surfactant protein D, and matrix metalloproteinase-2 as diagnostic biomarkers in patients with asbestosis and silicosis: A case–control study. BMC Pulmonary Medicine, 17, 32.
37. Tzang, C.-C., Lin, W.-C., Huang, E. S., Kang, Y.-F., Cheng, Y.-H., Tzang, B.-S., et al. (2025). Interstitial lung disease biomarkers: A systematic review and meta-analysis. Clinica Chimica Acta, 577, 120473. Bonella, F., Vegas Sanchez, M. C., d’Alessandro, M., Millan-Billi, P., Santos, R. F., Schröder, N., et al. (2025). Serum KL-6 as a biomarker to predict progression at one year in interstitial lung disease. Sci Rep, 15(1):35243.