Osteoporosis Screening: Applied Methods and Technological Trends

Oliveira, Mário Anderson de; Moraes, Raimes; Castanha, Everton B.; Prevedello, Alexandra Secreti; Filho, Jozué Vieira; Bussolaro, Frederico A.; Cava, David García

doi:10.1016/j.medengphy.2022.103887

Cited by 29 publications

(10 citation statements)

References 155 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Secure IoT communication is required to fetch data from the particular senor to the system. In [ [2]], it proposes integrating AI technologies like neural networks and fuzzy systems into a secure healthcare monitoring system. Physiological data from patients is collected, authenticated, and transmitted to Azure IoT Hub via GSM for analysis.…”

Section: Table 1 Unknown Sample Measurement and Validation With Stand...mentioning

confidence: 99%

Advances in Sensor-Based Detection and Machine Learning for Osteoporosis: A Comprehensive Survey of Diagnostic Techniques

Budyal,

Suresh

et al. 2024

IJRASET

View full text Add to dashboard Cite

One of the biggest challenges in the field of healthcare is the early identification of bone diseases. The increasing demand for diagnostic services often overwhelms current approaches, leading to increased healthcare costs, delayed identification, and patient discontent. To solve this problem, an Osteoporosis Early Detection System incorporating Biosensors and Machine Learning via the Internet of Things (IoT) is desperately needed. Osteoporosis difficulties and increased health risks are similar to congestion challenges that they might arise from delayed diagnosis. The scarcity of adequate technologies for early osteoporosis detection hinders effective healthcare delivery, especially in areas where the condition is more prevalent or during screenings that need quick identification. In order to enable prompt identification and notify medical professionals for preventive action, certain healthcare institutions have started including biosensors that can track changes in bone density. The development of these technologies could revolutionize the early diagnosis and treatment of osteoporosis and lessen the effects of the condition on patient’s health outcomes.

show abstract

Section: Table 1 Unknown Sample Measurement and Validation With Stand...mentioning

confidence: 99%

Advances in Sensor-Based Detection and Machine Learning for Osteoporosis: A Comprehensive Survey of Diagnostic Techniques

Budyal,

Suresh

et al. 2024

IJRASET

View full text Add to dashboard Cite

show abstract

“…FRAX -это компьютерный алгоритм, использующийся с 2008 г., который на основании выявления клинических факторов риска оценивает 10-летнюю вероятность перелома бедренной кости и основных патологических переломов (суммарно переломы плечевой Обзор литературы / Literature review и лучевой костей и тел позвонков) [12]. Ключевыми факторами риска ОП, которые анализируются алгоритмом FRAX, являются предшествующий перелом, перелом бедра у родителей, курение, прием ГКС, алкоголизм, наличие ревматоидного артрита и некоторых других причин вторичного ОП (сахарный диабет 1 типа (СД 1 типа), мальабсорбция, цирроз печени, гипотиреоз и др.).…”

Section: диагностика остеопорозаunclassified

Diagnosis and treatment of osteoporosis: a paradigm shift

Lobanova,

Ushanova

2024

FOCUS Endocrinology

View full text Add to dashboard Cite

Основным клиническим проявлением остеопороза (ОП) является низкоэнергетичный перелом. Перелом, в свою очередь, ассоциирован с высоким риском инвалидизации и летальности. В связи с этим целью лечения ОП является снижение риска развития переломов. Именно поэтому эксперты из Великобритании в 2019 г. разработали и внедрили в практику систему стратификации риска переломов, на основании которой принимается решение о лечении пациентов с ОП. Так, пациентам с очень высоким риском переломов необходимо безотлагательное назначение анаболической терапии с последующим назначением антирезорбтивных препаратов. Пациентам с высоким риском -назначение антирезорбтивной терапии, а при ее неэффективности -назначение анаболической терапии. Пациентам с низким и промежуточным риском рекомендовано динамическое наблюдение с постоянной переоценкой риска развития переломов. Это заключение полностью изменяет существующую парадигму лечения ОП.

show abstract

“…3 The high prevalence of OP in postmenopausal women is attributed to the fluctuation of hormones, estrogen in particular, and the decrease in bone mineral content since menopause. 4,5 PMOP patients face the risk of osteoporotic fractures as high as 30-40%, making it one of the most urgent clinical problems to be solved. 6 Bone mineral density (BMD) assessed by dual X-ray absorptiometry (DXA) and the incidence of osteoporotic fractures are the basis of the operational diagnosis of OP.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Plasma serotonin precursors and metabolite are correlated with bone mineral density and bone turnover markers in patients with postmenopausal osteoporosis

Feng,

Song,

Liu

et al. 2024

J Orthop Surg (Hong Kong)

View full text Add to dashboard Cite

Background Serotonin (5-HT) precursors regulate bone remodeling. This study aims to investigate the correlation of plasma 5-HT precursors and metabolite with bone mineral density (BMD) and bone turnover markers in postmenopausal osteoporosis (PMOP) patients. Methods The age, body mass index (BMI), and years since menopause (YSM) were documented for 348 postmenopausal women in normal/osteopenia/osteoporosis (OP) groups, with lumbar spine and femoral neck BMD measured. Serum bone turnover markers (PINP/β-CTX) and plasma 5-HT, 5-HT precursors (Trp/5-HTP) and metabolite (5-HIAA) were measured by ELISA. OP patients were allocated to high/low expression groups following ROC analysis of 5-HT/Trp/5-HTP/5-HIAA. The relationship of plasma 5-HT/Trp/5-HTP/5-HIAA, BMD, and bone turnover markers with PMOP was analyzed using logistic regression analysis. The correlation of plasma 5-HT/Trp/5-HTP/5-HIAA with BMD and bone turnover markers was analyzed using Pearson’s correlation analysis, followed by logistic regression analysis of the relationship between plasma 5-HT/Trp/5-HTP/5-HIAA and BMD, bone turnover markers and PMOP. Results BMI, YSM, BMD and PINP, and β-CTX levels differed among groups. Levels of plasma 5-HT precursors/metabolite were increased in OP patients. Individuals with high 5-HT precursors/metabolite levels had low BMD and high PINP/β-CTX levels. The 5-HT precursors/metabolite negatively-correlated with BMD and positively-correlated with PINP/β-CTX. BMI, YSM, BMD, and PINP/β-CTX/Trp/5-HTP/5-HT related to PMOP and were independent risk factors for OP. Conclusion Plasma 5-HT precursors and metabolite negatively-correlate with BMD and positively-correlate with PINP/β-CTX in PMOP patients. Peripheral 5-HT precursors and metabolite level may be a new direction of treatment of PMOP and bone metabolism-related disorders.

show abstract

Osteoporosis Screening: Applied Methods and Technological Trends

Cited by 29 publications

References 155 publications

Advances in Sensor-Based Detection and Machine Learning for Osteoporosis: A Comprehensive Survey of Diagnostic Techniques

Advances in Sensor-Based Detection and Machine Learning for Osteoporosis: A Comprehensive Survey of Diagnostic Techniques

Diagnosis and treatment of osteoporosis: a paradigm shift

Plasma serotonin precursors and metabolite are correlated with bone mineral density and bone turnover markers in patients with postmenopausal osteoporosis

Contact Info

Product

Resources

About