Decision Support System for the Design Process of Apatite Biopolymer Composite Parts

[1] ISO/ASTM TR 52916:2022 “Additive manufacturing for medical − Data − Optimized medical image data”, 2022. Search in Google Scholar

[2] P. Celard, E.L. Iglesias, J.M. Sorribes-Fdez, R. Romero, A. Seara Vieira and L. Borrajo. “A survey on deep learning applied to medical images: from simple artificial neural networks to generative models”. Neural Comput & Applic., vol. 35, pp. 2291-2323, 2023. Search in Google Scholar

[3] M. Bahraminasab. “Challenges on optimization of 3D-printed bone scaffolds”. BioMed Eng OnLine., vol. 19, 69, 2020. Search in Google Scholar

[4] H.I. Park, J.H. Lee, and S.J. Lee. “The comprehensive on-demand 3D bio-printing for composite reconstruction of mandibular defects”. Maxillofac Plast Reconstr Surg, vol. 44, 31, 2022. Search in Google Scholar

[5] B. Zhang, Y. He, J. Liu, J. Shang, Ch. Chen, T. Wang, M. Chen, Y. Li, G. Gong, J. Fang, Z. Zhao and J. Guo. “Advancing collagen-based biomaterials for oral and craniofacial tissue regeneration”. Collagen & Leather, vol. 5, 14, 2023. Search in Google Scholar

[6] L. Sukhodub, A. Panda, K. Dyadyura, I. Pandova and T. Krenicky. “The design criteria for biodegradable magnesium alloy implants.” MM Science Journal, 2018, 2018 (December), pp. 2673-2679, 2020. Search in Google Scholar

[7] A. Panda, K. Dyadyura, J. Valíček, M. Harničárová, M. Kušnerová, T. Ivakhniuk, L. Hrebenyk, O. Sapronov, V. Sotsenko, P. Vorobiov, V. Levytskyi, A. Buketov and I. Pandová. “Ecotoxicity Study of New Composite Materials Based on Epoxy Matrix DER-331 Filled with Biocides Used for Industrial Applications”. Polymers, vol. 14, no. 16, 3275, 2022. Search in Google Scholar

[8] F. Camacho-Alonso, C. Martínez-Ortiz, L. Plazas-Buendía, A.M. Mercado-Díaz, C. Vilaplana-Vivo, J.A. Navarro, A.J. Buendía, J.J. Merino and Y. Martínez-Beneyto. “Bone union formation in the rat mandibular symphysis using hydroxyapatite with or without simvastatin: effects on healthy, diabetic, and osteoporotic rats”. Clin Oral Invest, vol. 24, pp. 1479-1491, 2020. Search in Google Scholar

[9] F. Ramzan, A. Salim and I. Khan. “Osteochondral Tissue Engineering Dilemma: Scaffolding Trends in Regenerative Medicine”. Stem Cell Rev and Rep, vol. 19, pp. 1615-1634, 2023. Search in Google Scholar

[10] L. Sukhodub, A. Panda, L. Suchodub, M. Kumeda, K. Dyadyura and I. Pandova. “Hydroxyapatite and zinc oxide based two-layer coating, deposited on Ti6Al4V substrate.” MM Science Journal, 2019 (December), pp. 3494-3499, 2019. Search in Google Scholar

[11] S.V.S. Prasad, B.Ch. Rao, M.K. Rao, K.R. Kumar, S.D.V. Prasad, Ch. Ramesh. “Medical image segmentation using an optimized three-tier quantum convolutional neural network trained with hybrid optimization approach”. Mul-timed Tools Appl, vol. 83, pp. 38083-38108, 2024. Search in Google Scholar

[12] N. Jitani, B.J. Singha, G. Barman, A. Talukdar, R. Sarmah and D.K. Bhattacharyya. “Medical image segmentation using automated rough density approach”. Multimed Tools Appl., vol. 83, pp. 39677-39705, 2024. Search in Google Scholar

[13] S. Minaee, Y. Boykov, F. Porikli, A. Plaza, N. Kehtarnavaz and D. Terzopoulos. “Image Segmentation Using Deep Learning: A Survey”. IEEE Trans Pattern Anal Mach Intell, vol. 44, no. 7, pp. 3523-3542, 2022. Search in Google Scholar

[14] S. Iqbal, A.N. Qureshi, J. Li, and T. Mahmood. “On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks”. Arch Computat Methods Eng., vol. 30, pp. 3173-3233, 2023. Search in Google Scholar

[15] N. Thakur, P. Kumar and A. Kumar. “A systematic review of machine and deep learning techniques for the identification and classification of breast cancer through medical image modalities”. Multimed Tools Appl., vol. 83, pp. 35849-35942, 2024. Search in Google Scholar

[16] M. Safari, A. Fatemi and L. Archambault. “MedFusionGAN: multimodal medical image fusion using an unsupervised deep generative adversarial network”. BMC Med Imaging, vol. 23, 203, 2023. Search in Google Scholar

[17] A.S. Lundervold and A. Lundervold. “An overview of deep learning in medical imaging focusing on MRI”. Z Med Phys., vol. 29, no. 2, pp. 102-127, 2019. Search in Google Scholar

[18] V, Nainamalai, M. Lippert, H. Brun, O.J. Elle and R.P. Kumar. “Local integration of deep learning for advanced visualization in congenital heart disease surgical planning”. Intell Based Med., vol. 6, 100055, 2022. Search in Google Scholar

[19] M. Akazawa and K. Hashimoto. “Artificial intelligence in gynecologic cancers: current status and future challenges – a systematic review”. Artif Intell Med., vol. 120, 102164, 2021. Search in Google Scholar

[20] V.S. de Siqueira, M.M. Borges, R.G. Furtado, C.N. Dourado and R.M. da Costa. “Artificial intelligence applied to support medical decisions for the automatic analysis of echo-cardiogram images: a systematic review”. Artif Intell Med., vol. 120, 102165, 2021. Search in Google Scholar

[21] T. Fernando, H. Gammulle, S. Denman, S. Sridharan and C. Fookes. “Deep learning for medical anomaly detection – a survey”. ACM Comput Surv., vol. 54, no. 7, 2021. Search in Google Scholar

[22] J. Chen J, K. Li, Z. Zhang, K. Li and P.S. Yu. “A survey on applications of artificial intelligence in fighting against COVID-19”. ACM Comput Surv., vol 54, no. 8, 2021. Search in Google Scholar

[23] M. Sah and C. Direkoglu. “A survey of deep learning methods for multiple sclerosis identification using brain mri images”. Neural Comput Appl., vol. 34, no. 10, pp. 7349-7373, 2022. Search in Google Scholar

[24] M.A. Abdou. “Literature review: efficient deep neural networks techniques for medical image analysis. Neural Comput Appl., vol. 34, no. 8, pp. 5791-5812, 2022. Search in Google Scholar

[25] A. Kaur, L. Kaur and A. Singh. “GA-UNet: UNet-based framework for segmentation of 2D and 3D medical images applicable on heterogeneous datasets”. Neural Comput & Applic., vol. 33, pp. 14991-15025, 2021. Search in Google Scholar

[26] M.S. Hossain, G.M. Shahriar, M.M.M. Syeed, M.F. Uddin, M. Hasan, S. Shivam and S. Advani. “Region of interest (ROI) selection using vision transformer for automatic analysis using whole slide images”. Sci Rep., vol. 13, 11314, 2023. Search in Google Scholar

[27] I. Pandová, M. Rimár, A. Panda, J. Valíček, M. Kušnerová and M. Harničárová. “A study of using natural sorbent to reduce iron cations from aqueous solutions.” International Journal of Environmental Research and Public Health, 17 (10), 3686, 2020. Search in Google Scholar

[28] A. Panda, V.M. Anisimov, V.V. Anisimov, K. Dyadyura and I. Pandova. “Increasing of wear resistance of linear block-polyurethanes by thermal processing methods.” MM Science Journal, 2021, October, pp. 4731-4735, 2021. Search in Google Scholar

[29] A. Panda, M. Prislupčák and I. Pandová. “Progressive technology diagnostics and factors affecting machinability.” Applied Mechanics and Materials, 616, pp. 183-190, 2014. Search in Google Scholar

[30] R. Cantor and T.A. Curtis. “Prosthetic management of edentulous mandibulectomy patients. Part II. Clinical procedures”. J. Prosthet. Dent., vol. 25, pp. 546-555, 1971. Search in Google Scholar

[31] R. Cantor and T.A. Curtis. “Prosthetic management of edentulous mandibulectomy patients. Part III. Clinical evaluation”. J. Prosthet. Dent., vol. 25, pp. 670-678, 1971. Search in Google Scholar

[32] D. Dmitrishin, G. Lesaja, I. Skrinnik and A. Stokolos. “A new method for finding cycles by semilinear control”. Physics Letters, Section A: General, Atomic and Solid State Physics, vol. 383, no. 16, pp. 1871-1878, 2019. Search in Google Scholar