Franchini M, Mannucci PM (2014) The history of hemophilia. Semin Thromb Hemost 40:571–576

Article  CAS  PubMed  Google Scholar 

Hoyer LW (1994) Hemophilia a. New England Journal of Medicine, Mass Medical Soc 330:38–47

CAS  Google Scholar 

Berntorp E, Shapiro AD (2012) Modern haemophilia care. The Lancet, Elsevier 379:1447–1456

Article  Google Scholar 

Iorio A, Stonebraker JS, Chambost H, Makris M, Coffin D, Herr C, Germini F (2019) Data and demographics committee of the world federation of hemophilia. establishing the prevalence and prevalence at birth of hemophilia in males: a meta-analytic approach using national registries. Ann Intern Med 171:540–546

Article  PubMed  Google Scholar 

Ferreira AA, Leite ICG, Bustamante-Teixeira MT, Corrêa CSL, da Cruz DT, Rodrigues D et al (2013) Health-related quality of life in hemophilia: results of the hemophilia-specific quality of life index (haem-a-qol) at a brazilian blood center. Revista brasileira de hematologia e hemoterapia, SciELO Brasil 35:314–318

Google Scholar 

Walsh M, Macgregor D, Stuckless S, Barrett B, Kawaja M, Scully M-F (2008) Health-related quality of life in a cohort of adult patients with mild hemophilia a. Journal of Thrombosis and Haemostasis, Elsevier 6:755–761

Article  CAS  Google Scholar 

Hoots, W.K. and Shapiro, A.D. (2014) Clinical Manifestations and Diagnosis of Hemophilia. UpToDate Nov, 11.

Bolton-Maggs PHB, Pasi KJ (2003) Haemophilias a and b. The Lancet, Elsevier 361:1801–1809

Article  CAS  Google Scholar 

Luck JV Jr, Silva M, Rodriguez-Merchan CE, Ghalambor N, Zahiri CA, Finn RS (2004) Hemophilic Arthropathy. JAAOS-Journal of the American Academy of Orthopaedic Surgeons, LWW 12:234–245

Article  Google Scholar 

Dehaven KE (1980) Diagnosis of acute knee injuries with hemarthrosis. The American journal of sports medicine, SAGE Publications 8:9–14

Article  CAS  Google Scholar 

Querol F, Rodriguez-Merchan EC (2012) The role of ultrasonography in the diagnosis of the musculo-skeletal problems of haemophilia. Haemophilia, Wiley Online Library 18:e215–e226

CAS  Google Scholar 

Bakeer N, Dover S, Babyn P, Feldman BM, von Drygalski A, Doria AS, Ignas DM, Abad A, Bailey C, Beggs I, Chang EY, Dunn A, Funk S, Gibikote S, Goddard N, Hilliard P, Keshava SN, Kruse-Jarres R, Li Y, Lobet S, Manco-Johnson M, Martinoli C, O’Donnell JS, Papakonstantinou O, Pergantou H, Poonnoose P, Querol F, Srivastava A, Steiner B, Strike K, Timmer M, Tyrrell PN, Vidarsson L, Blanchette VS (2021) Musculoskeletal Ultrasound in Hemophilia: Results and Recommendations from a Global Survey and Consensus Meeting. Research and Practice in Thrombosis and Haemostasis, Elsevier 5:e12531. https://doi.org/10.1002/RTH2.12531

Article  Google Scholar 

Nguyen S, Lu X, Ma Y, Du J, Chang EY, von Drygalski A (2018) Musculoskeletal Ultrasound for Intra-articular Bleed Detection: A Highly Sensitive Imaging Modality Compared with Conventional Magnetic Resonance Imaging. Journal of Thrombosis and Haemostasis, Elsevier 16:490–499. https://doi.org/10.1111/JTH.13930

Article  CAS  Google Scholar 

Dick, S. (2019) Artificial Intelligence. Harvard Data Sci Rev 1 (1).

Winston PH (1992) Artificial Intelligence. Addison-Wesley Longman Publishing Co., Inc

Google Scholar 

Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, Santamaría J, Fadhel MA, Al-Amidie M, Farhan L (2021) Review of Deep Learning: Concepts, CNN Architectures, Challenges, Applications, Future Directions. Journal of Big Data 8:1–74. https://doi.org/10.1186/S40537-021-00444-8

Article  Google Scholar 

Van Ginneken B, Setio AAA, Jacobs C, Ciompi F (2015) Off-the-Shelf Convolutional Neural Network Features for Pulmonary Nodule Detection in Computed Tomography Scans. IEEE 12th International Symposium on Biomedical Imaging (ISBI). https://doi.org/10.1109/ISBI.2015.7163869

Article  Google Scholar 

Lakhani P, Sundaram B (2017) Deep learning at chest radiography: Automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology, Radiological Society of North America 284:574–582

Google Scholar 

Bejnordi BE, Veta M, Van Diest PJ, Van Ginneken B, Karssemeijer N, Litjens G, Van Der Laak JAWM, Hermsen M, Manson QF, Balkenhol M et al (2017) Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. Jama, American Medical Association 318:2199–2210

Article  Google Scholar 

Zuluaga-Gomez J, Al Masry Z, Benaggoune K, Meraghni S, Zerhouni N (2021) A CNN-based methodology for breast cancer diagnosis using thermal images. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, Taylor & Francis 9:131–145. https://doi.org/10.1080/21681163.2020.1824685

Article  Google Scholar 

Chen L, Wu Y et al (2018) MRI Tumor Segmentation with Densely Connected 3D CNN. SPIE 10574:357–364. https://doi.org/10.1117/12.2293394

Article  Google Scholar 

Rouhi R, Jafari M, Kasaei S, Keshavarzian P (2015) Benign and Malignant Breast Tumors Classification Based on Region Growing and CNN Segmentation. Expert Systems with Applications, Pergamon 42:990–1002. https://doi.org/10.1016/J.ESWA.2014.09.020

Article  Google Scholar 

Reshi AA, Rustam F, Mehmood A, Alhossan A, Alrabiah Z, Ahmad A, Alsuwailem H, Choi GS (2021) An Efficient CNN Model for COVID-19 Disease Detection Based on X-Ray Image Classification. Complexity, John Wiley & Sons Ltd 2021:6621607. https://doi.org/10.1155/2021/6621607

Article  Google Scholar 

Soni M, Khan IR, Babu KS, Nasrullah S, Madduri A, Rahin SA (2022) Light Weighted Healthcare CNN Model to Detect Prostate Cancer on Multiparametric MRI. Computational Intelligence and Neuroscience, John Wiley & Sons Ltd 2022:5497120. https://doi.org/10.1155/2022/5497120

Article  Google Scholar 

Togacar, M., Comert, Z., Ergen, B. and Budak, U. (2019) Brain Hemorrhage Detection Based on Heat Maps, Autoencoder and CNN Architecture. 1st International Informatics and Software Engineering Conference: Innovative Technologies for Digital Transformation, IISEC 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/UBMYK48245.2019.8965576.

Saric, M., Russo, M., Stella, M. and Sikora, M. (2019) CNN-Based Method for Lung Cancer Detection in Whole Slide Histopathology Images. 2019 4th International Conference on Smart and Sustainable Technologies, SpliTech 2019, Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.23919/SPLITECH.2019.8783041.

Park SH (2021) Artificial Intelligence for Ultrasonography: Unique Opportunities and Challenges. Ultrasonography, Korean Society of Ultrasound in Medicine 40:3

Google Scholar 

Shen YT, Chen L, Yue WW, Xu HX (2021) Artificial Intelligence in Ultrasound. European Journal of Radiology, Elsevier 139:109717. https://doi.org/10.1016/J.EJRAD.2021.109717

Article  Google Scholar 

Shahzad A, Mushtaq A, Sabeeh AQ, Ghadi YY, Mushtaq Z, Arif S et al (2023) Automated Uterine Fibroids Detection in Ultrasound Images Using Deep Convolutional Neural Networks. Healthcare. https://doi.org/10.3390/HEALTHCARE11101493

Article  PubMed  PubMed Central  Google Scholar 

Inui A, Mifune Y, Nishimoto H, Mukohara S, Fukuda S, Kato T, Furukawa T, Tanaka S, Kusunose M, Takigami S, Ehara Y, Kuroda R (2023) Detection of Elbow OCD in the Ultrasound Image by Artificial Intelligence Using YOLOv8. Appl Sci. https://doi.org/10.3390/APP13137623

Article  Google Scholar 

Gupta P, Basu S, Rana P, Dutta U, Soundararajan R, Kalage D, Chhabra M, Singh S, Yadav TD, Gupta V, Kaman L, Das CK, Gupta P, Saikia UN, Srinivasan R, Sandhu MS, Arora C (2024) Deep-Learning Enabled Ultrasound Based Detection of Gallbladder Cancer in Northern India: A Prospective Diagnostic Study. The Lancet Regional Health - Southeast Asia, Elsevier Ltd 24:100279. https://doi.org/10.1016/j.lansea.2023.100279

Article  Google Scholar 

Sahu A, Das PK, Meher S (2023) High Accuracy Hybrid CNN Classifiers for Breast Cancer Detection Using Mammogram and Ultrasound Datasets. Biomedical Signal Processing and Control, Elsevier 80:104292. https://doi.org/10.1016/J.BSPC.2022.104292

Article  Google Scholar 

Al-Battal, A.F., Gong, Y., Xu, L., Morton, T., Du, C., Bu, Y., Lerman, I.R., Madhavan, R. and Nguyen, T.Q. (2021) A CNN Segmentation-Based Approach to Object Detection and Tracking in Ultrasound Scans with Application to the Vagus Nerve Detection. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Institute of Electrical and Electronics Engineers Inc., 3322–3327. https://doi.org/10.1109/EMBC46164.2021.9630522.

Karras, T., Aila, T., Laine, S. and Lehtinen, J. (2017) Progressive Growing of GANs for Improved Quality, Stability, and Variation. CoRR, abs/1710.10196. http://arxiv.org/abs/1710.10196.

Sun H, Lu Z, Fan R, Xiong W, Xie K, Ni X, Yang J (2021) Research on Obtaining Pseudo CT Images Based on Stacked Generative Adversarial Network. Quant Imaging Med Surg. https://doi.org/10.21037/QIMS-20-1019

Article  PubMed  PubMed Central  Google Scholar 

Chen RJ, Lu MY, Chen TY, Williamson DFK, Mahmood F (2021) Synthetic Data in Machine Learning for Medicine and Healthcare. Nature Biomedical Engineering, Nature Publishing Group UK London 5:493–497

Article  Google Scholar 

Nagao A, Inagaki Y, Nogami K, Yamasaki N, Iwasaki F, Liu Y, Murakami Y, Ito T, Takedani H (2024) Artificial Intelligence-Assisted Ultrasound Imaging in Hemophilia: Research, Development, and Evaluation of Hemarthrosis and Synovitis Detection. Research and Practice in Thrombosis and Haemostasis, Elsevier 8:102439. https://doi.org/10.1016/J.RPTH.2024.102439

Article  Google Scholar 

Gualtierotti R, Arcudi S, Ciavarella A, Colussi M, Mascetti S, Bettini C, Peyvandi F (2022) A Computer-Aided Diagnosis Tool for the Detection of Hemarthrosis By Remote Joint Ultrasound in Patients with Hemophilia. Blood, American Society of Hematology 140:464–465. https://doi.org/10.1182/BLOOD-2022-163690

Article  Google Scholar 

Katakis S, Barotsis N, Kakotaritis A, Tsiganos P, Economou G, Panagiotopoulos E, Panayiotakis G (2023) Generation of Musculoskeletal Ultrasound Images with Diffusion Models. BioMedInformatics. https://doi.org/10.3390/BIOMEDINFORMATICS3020027

Article  Google Scholar 

Alsinan AZ, Rule C, Vives M, Patel VM, Hacihaliloglu I (2020) GAN-Based Realistic Bone Ultrasound Image and Label Synthesis for Improved Segmentation. Lect Notes Comput Sci. https://doi.org/10.1007/978-3-030-59725-2_77/FIGURES/2

Article  Google Scholar 

Escobar M, Castillo A, Romero A, Arbeláez P (2020) Ultragan: Ultrasound Enhancement through Adversarial Generation. Lect Notes Comput Sci. https://doi.org/10.1007/978-3-030-59520-3_13/TABLES/2

Article  Google Scholar 

Mendez, M., Sundararaman, S., Probyn, L. and Tyrrell, P.N. (2023) Approaches and Limitations of Machine Learni