Kemik Kaybının Erken Tanısı İçin Alternatif Yöntem: Elektrokimyasal Biyosensörler

Rani S, Bandyopadhyay-Ghosh S, Ghosh SB, Liu G. Advances in Sensing Technologies for Monitoring of Bone Health. Biosensors; 2020;10: 42.

Kuo TR, Chen CH. Bone biomarker for the clinical assessment of osteoporosis: recent developments and future perspectives. Biomark Res.; 2017;18.

Pisani P, Renna MD, Conversano F, Casciaro E, Di Paola M, Quarta E, et al. Major osteoporotic fragility fractures: risk factor updates and societal impact. World J Orthop.; 2016;7: 171.

Alzubaidi MA, Otoom M. A comprehensive study on feature types for osteoporosis classifcation in dental panoramic radiographs. Comput. Methods Programs Biomed.; 2020;188: 105301.

Singh A, Dutta MK, Jennane R, Lespessailles E. Classifcation of the trabecular bone structure of osteoporotic patients using machine vision. Comput. Biol. Med.; 2017;91: 148–158.

Leibson CL, Tosteson ANA, Gabriel SE, Ransom JE, Melton LJ. Mortality, disability, and nursing home use for persons with and without hip fracture: a population-based study. J Am Geriatr Soc.; 2002;50: 1644–50.

Khan SS, Jayan AS, Nageswaran S. An image processing algorithm to estimate bone mineral density using digital X-ray images. 2017 Second International Conference on Electrical, Computer and Communication Technologies (ICECCT): IEEE; 2017. pp. 1–4.

Lestari S, Diqi M, Widyaningrum R. Masurement of maximum value of dental radiograph to predict the bone mineral density. 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI): IEEE; 2017. pp. 1–4.

Hernandez NR, Escareno MCH, Rendon JRM. Image analysis tool with laws’ masks to bone texture. 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA): IEEE; 2017. pp. 691–694.

Hernández NR. Structural analysis of textures based on LAW´ s flters. 2016 IEEE XXIII International Congress on Electronics, Electrical Engineering and Computing (INTERCON): IEEE; 2016. pp. 1–5.

Lee JS, Adhikari S, Liu L, Jeong HG, Kim H, Yoon SJ. Osteoporosis detection in panoramic radiographs using a deep convolutional neural network-based computer-assisted diagnosis system: a preliminary study. Dentomaxillofacial Radiol.; 2019;48: 20170344.

Lee KS, Jung SK, Ryu JJ, Shin SW, Choi J. Evaluation of transfer learning with deep convolutional neural networks for screening osteoporosis in dental panoramic radiographs. J. Clin. Med.; 2020;9: 392.

Alzubaidi MA, Otoom M. A comprehensive study on feature types for osteoporosis classifcation in dental panoramic radiographs. Comput. Methods Programs Biomed.; 2020;188: 105301.

Chikhalekar AT. Analysis of image processing for digital X-ray. Int. Res. J. Eng. Technol. (IRJET); 2016;3(5): 2356–2395.

Arabi PM, Joshi G, Reddy RN. Categorizing healthy, osteopenic andosteoporotic bones by white pixel calculation. 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT): IEEE; 2017. pp. 1–4.

Sam M, Areeckal AS. Early diagnosis of osteoporosis using active appearance model and metacarpal radiogrammetry. 2017 13th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS): IEEE; 2017. pp. 173–178.

Kabiraj A, Meena T, Reddy PB, Roy S. Detection and classifcation of lung disease using deep learning architecture from X-ray images. in Advances in Visual Computing 17th International Symposium, ISVC 2022: San Diego, CA, USA, 3–5 Oct, 2022: Proceedings, Part I (Springer International Publishing, Cham 2022. pp. 444–455.

Lang TF, Guglielmi G, Van Kuijk C, De Serio A, Cammisa M, Genant HK. Measurement of bone mineral density at the spine and proximal femur by volumetric quantitative computed tomography and dual-energy X-ray Absorptiometry in elderly women with and without vertebral fractures. Bone; 2002;30: 247–50.

Adams JE. Radiogrammetry and radiographic Absorptiometry. Radiol Clin N Am.; 2010;48: 531–40.

Alexeeva L, Burkhardt P, Christiansen C, Cooper C, Delmas P, Johnell O, et al. Assessment of fracture risk and its application to screening for postmenopausal osteoporosis: report of a WHO study group [meeting held in Rome from 22 to 25 June 1992].World Health Organization; 1994. p. 1–129.

Pisani P, Greco A, Renna M, Conversano F, Casciaro E, Quarta L, et al. An innovative ultrasound-based method for the identification of patients at high fracture risk. Proceedings of the 3rd Imeko TC13 Symposium on Measurements in Biology and Medicine. New Frontiers in Biomedical Measurements: 2014. p. e50–e53.

Conversano F, Franchini R, Greco A, Soloperto G, Chiriaco F, Casciaro E, et al. A novel untrasound methodology for estimating spine mineral density. Ultrasound Med Biol.; 2015;41: 281–300.

Mandl P, Kainberger F, Hitz MF. Imaging in osteoporosis in rheumatic diseases. Best Pract. Res. Clin. Rheumatol.; 2016;30: 751–765.

Schneider R. Imaging of osteoporosis. Rheum. Dis. Clin.; 2013;39: 609–631.

Roy S, Bhattacharyya D, Bandyopadhyay SK, Kim TH. An improved brain MR image binarization method as a preprocessing for abnormality detection and features extraction. Front. Comput. Sci.; 2017;11: 717–727.

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Handmaker H, Leonards R. The bone scan in inflammatory osseous disease. Semin. Nucl. Med.; 1976;6: 95–105.

Gafni RI, Baron J. Overdiagnosis of osteoporosis in children due to misinterpretation of Dual-energy x-ray absorptiometry (DEXA). J. Pediatr.; 2004;144: 253–257.

Bartl R, Bartl C. The Osteoporosis Manual: Prevention, Diagnosis and Management. Cham, Switzerland: Springer; 2019. pp. 67–75.

Cummings SR, Bates D, Black DM. Clinical use of bone densitometry: Scientific review. J. Am. Med. Assoc.; 2002;288: 1889–1897.

Kiuru MJ, Pihlajamaki HK, Hietanen HJ, Ahovuo JA. MR imaging, bone scintigraphy, and radiography in bone stress injuries of the pelvis and the lower extremity. Acta Radiol.; 2002;43: 207–212.

Kanis JA, McCloskey EV, Johansson H, Oden A, Melton LJ, Khaltaev N. A reference standard for the description of osteoporosis. Bone; 2008;42: 467–475.

Vijayanathan S, Butt S, Gnanasegaran G, Groves AM. Advantages and Limitations of Imaging the Musculoskeletal System by Conventional Radiological, Radionuclide, and Hybrid Modalities. Semin. Nucl. Med.; 2009;39: 357–368.

Eisenhauer A, Müller M, Heuser A, Kolevica A, Glüer CC, Both M, Laue C, Hehn U, Kloth S, Shroff, R, et al. Calcium isotope ratios in blood and urine: A new biomarker for the diagnosis of osteoporosis. Bone Rep.; 2019;10: 100200.

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Rani S, Bandyopadhyay-Ghosh S, Ghosh SB, Liu G. Advances in Sensing Technologies for Monitoring of Bone Health. Biosensors; 2020;10: 42.

Kuo TR, Chen CH. Bone biomarker for the clinical assessment of osteoporosis: recent developments and future perspectives. Biomark Res.; 2017;18.

Pisani P, Renna MD, Conversano F, Casciaro E, Di Paola M, Quarta E, et al. Major osteoporotic fragility fractures: risk factor updates and societal impact. World J Orthop.; 2016;7: 171.

Alzubaidi MA, Otoom M. A comprehensive study on feature types for osteoporosis classifcation in dental panoramic radiographs. Comput. Methods Programs Biomed.; 2020;188: 105301.

Singh A, Dutta MK, Jennane R, Lespessailles E. Classifcation of the trabecular bone structure of osteoporotic patients using machine vision. Comput. Biol. Med.; 2017;91: 148–158.

Leibson CL, Tosteson ANA, Gabriel SE, Ransom JE, Melton LJ. Mortality, disability, and nursing home use for persons with and without hip fracture: a population-based study. J Am Geriatr Soc.; 2002;50: 1644–50.

Khan SS, Jayan AS, Nageswaran S. An image processing algorithm to estimate bone mineral density using digital X-ray images. 2017 Second International Conference on Electrical, Computer and Communication Technologies (ICECCT): IEEE; 2017. pp. 1–4.

Lestari S, Diqi M, Widyaningrum R. Masurement of maximum value of dental radiograph to predict the bone mineral density. 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI): IEEE; 2017. pp. 1–4.

Hernandez NR, Escareno MCH, Rendon JRM. Image analysis tool with laws’ masks to bone texture. 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA): IEEE; 2017. pp. 691–694.

Hernández NR. Structural analysis of textures based on LAW´ s flters. 2016 IEEE XXIII International Congress on Electronics, Electrical Engineering and Computing (INTERCON): IEEE; 2016. pp. 1–5.

Lee JS, Adhikari S, Liu L, Jeong HG, Kim H, Yoon SJ. Osteoporosis detection in panoramic radiographs using a deep convolutional neural network-based computer-assisted diagnosis system: a preliminary study. Dentomaxillofacial Radiol.; 2019;48: 20170344.

Lee KS, Jung SK, Ryu JJ, Shin SW, Choi J. Evaluation of transfer learning with deep convolutional neural networks for screening osteoporosis in dental panoramic radiographs. J. Clin. Med.; 2020;9: 392.

Alzubaidi MA, Otoom M. A comprehensive study on feature types for osteoporosis classifcation in dental panoramic radiographs. Comput. Methods Programs Biomed.; 2020;188: 105301.

Chikhalekar AT. Analysis of image processing for digital X-ray. Int. Res. J. Eng. Technol. (IRJET); 2016;3(5): 2356–2395.

Arabi PM, Joshi G, Reddy RN. Categorizing healthy, osteopenic andosteoporotic bones by white pixel calculation. 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT): IEEE; 2017. pp. 1–4.

Sam M, Areeckal AS. Early diagnosis of osteoporosis using active appearance model and metacarpal radiogrammetry. 2017 13th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS): IEEE; 2017. pp. 173–178.

Kabiraj A, Meena T, Reddy PB, Roy S. Detection and classifcation of lung disease using deep learning architecture from X-ray images. in Advances in Visual Computing 17th International Symposium, ISVC 2022: San Diego, CA, USA, 3–5 Oct, 2022: Proceedings, Part I (Springer International Publishing, Cham 2022. pp. 444–455.

Lang TF, Guglielmi G, Van Kuijk C, De Serio A, Cammisa M, Genant HK. Measurement of bone mineral density at the spine and proximal femur by volumetric quantitative computed tomography and dual-energy X-ray Absorptiometry in elderly women with and without vertebral fractures. Bone; 2002;30: 247–50.

Adams JE. Radiogrammetry and radiographic Absorptiometry. Radiol Clin N Am.; 2010;48: 531–40.

Alexeeva L, Burkhardt P, Christiansen C, Cooper C, Delmas P, Johnell O, et al. Assessment of fracture risk and its application to screening for postmenopausal osteoporosis: report of a WHO study group [meeting held in Rome from 22 to 25 June 1992].World Health Organization; 1994. p. 1–129.

Pisani P, Greco A, Renna M, Conversano F, Casciaro E, Quarta L, et al. An innovative ultrasound-based method for the identification of patients at high fracture risk. Proceedings of the 3rd Imeko TC13 Symposium on Measurements in Biology and Medicine. New Frontiers in Biomedical Measurements: 2014. p. e50–e53.

Conversano F, Franchini R, Greco A, Soloperto G, Chiriaco F, Casciaro E, et al. A novel untrasound methodology for estimating spine mineral density. Ultrasound Med Biol.; 2015;41: 281–300.

Mandl P, Kainberger F, Hitz MF. Imaging in osteoporosis in rheumatic diseases. Best Pract. Res. Clin. Rheumatol.; 2016;30: 751–765.

Schneider R. Imaging of osteoporosis. Rheum. Dis. Clin.; 2013;39: 609–631.

Roy S, Bhattacharyya D, Bandyopadhyay SK, Kim TH. An improved brain MR image binarization method as a preprocessing for abnormality detection and features extraction. Front. Comput. Sci.; 2017;11: 717–727.

Van den Wyngaert T, Strobel K, Kampen WU., Kuwert T, Van der Bruggen W, Mohan HK, Gnanasegaran G, Delgado-Bolton R, Weber WA, Beheshti M. The EANM practice guidelines for bone scintigraphy. Eur. J. Nucl. Med. Mol. Imaging; 2016;43: 1723–1738.

Brenner AI, Koshy J, Morey J, Lin C, Dipoce J. The bone scan. Semin. Nucl. Med.; 2012;42: 11–26.

Handmaker H, Leonards R. The bone scan in inflammatory osseous disease. Semin. Nucl. Med.; 1976;6: 95–105.

Gafni RI, Baron J. Overdiagnosis of osteoporosis in children due to misinterpretation of Dual-energy x-ray absorptiometry (DEXA). J. Pediatr.; 2004;144: 253–257.

Bartl R, Bartl C. The Osteoporosis Manual: Prevention, Diagnosis and Management. Cham, Switzerland: Springer; 2019. pp. 67–75.

Cummings SR, Bates D, Black DM. Clinical use of bone densitometry: Scientific review. J. Am. Med. Assoc.; 2002;288: 1889–1897.

Kiuru MJ, Pihlajamaki HK, Hietanen HJ, Ahovuo JA. MR imaging, bone scintigraphy, and radiography in bone stress injuries of the pelvis and the lower extremity. Acta Radiol.; 2002;43: 207–212.

Kanis JA, McCloskey EV, Johansson H, Oden A, Melton LJ, Khaltaev N. A reference standard for the description of osteoporosis. Bone; 2008;42: 467–475.

Vijayanathan S, Butt S, Gnanasegaran G, Groves AM. Advantages and Limitations of Imaging the Musculoskeletal System by Conventional Radiological, Radionuclide, and Hybrid Modalities. Semin. Nucl. Med.; 2009;39: 357–368.

Eisenhauer A, Müller M, Heuser A, Kolevica A, Glüer CC, Both M, Laue C, Hehn U, Kloth S, Shroff, R, et al. Calcium isotope ratios in blood and urine: A new biomarker for the diagnosis of osteoporosis. Bone Rep.; 2019;10: 100200.

Morgan, JLL, Gordon, GW, Arrua, RC, Skulan, JL, Anbar, A.D, Bullen, TD. High-precision measurement of variations in calcium isotope ratios in urine by multiple collector inductively coupled plasma mass spectrometry. Anal Chem.; 2011; 15 (18): 6956-62.

Bover J, Ureña-Torre P, Alonso AML, Torregrosa JV, Rodríguez-García M, Castro-Alonso C, Górriz JL, Benito S, López-Báez V, Cora MJL, et al. Osteoporosis, bone mineral density and CKD-MBD (II): Therapeutic implications. Nefrologia; 2019;39: 227–242.

Alfaro F, Weiss L, Campbell P, Miller M, Fedder GK. Design of a multi-axis implantable MEMS sensor for intraosseous bone stress monitoring. J. Micromech. Microeng.; 2009;19: 085016.

Heaney RP, Gallagher JC, Johnston CC, Neer R, Parfitt AM, Whedon GD. Calcium nutrition and bone health in the elderly. Am. J. Clin. Nutr.; 1982;36: 986–1013.

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