Banca de DEFESA: GABRIELA DE ARAÚJO ALBUQUERQUE

Uma banca de DEFESA de DOUTORADO foi cadastrada pelo programa.
STUDENT : GABRIELA DE ARAÚJO ALBUQUERQUE
DATE: 21/09/2023
TIME: 09:00
LOCAL: Laboratório de Inovação Tecnológica em Saúde (LAIS)
TITLE:

Machine Learning Aplicado a Triagem de Osteoporose: modelo baseado em atenuação de ondas eletromagnéticas

KEY WORDS:

Densitometry, Osteoporosis, Osseus, Machine Learning, Random Forest.

PAGES: 60
BIG AREA: Engenharias
AREA: Engenharia Biomédica
SUMMARY:

Osteoporosis is a silent and still underdiagnosed condition, with a mortality rate higher than several types of cancer, especially when patients suffer fractures. The gold standard equipment for the diagnosis of densitometry, Dual-energy X-ray absorptiometry (DXA, or DEXA), which requires an invasive and costly procedure, is scarce in countries considered middle or low-income, thus hindering timely access to diagnosis. In this context, a portable device, known in the literature as Osseus, was developed for the screening of patients who need the densitometry exam, i.e., to qualify the referrals of exams to the DEXA equipment. The thesis aimed to validate the Osseus device using machine learning techniques. For this, the planning and data collection of 505 patients who underwent the DEXA exam and the test on the Osseus device were carried out, of which 21.8% were healthy and 78.2% were diseased (they had low bone mineral density or osteoporosis). Therefore, to implement the studies and develop the research, the database was separated into 80% for training and validation (5-fold cross-validation) and 20% for testing. The performance obtained in the test base with the best model (Random Forest) corresponded to sensitivity=0.853, specificity=0.871, and F1(harmonic average of precision and recall rate)=0.859. The results showed that the most relevant variables to indicate the individual health status were age, body mass index (BMI), and the attenuation measured in the Osseus. When compared to the results of DEXA scans, the model has proven to be effective and consistent in screening individuals with osteoporosis and facilitating early diagnosis of the disease, which consequently entails improved productivity and reduced costs for surgery, treatment, and hospitalization. This way, by qualifying the referral of patients from primary care to the specialized network, the Osseus can improve the osteoporosis care network and consolidate itself as a resource of easy access in primary care, also impacting the reduction of waiting lines in the specialized network of the BrazilianNational Health System (SUS).

COMMITTEE MEMBERS:
Presidente - 2488270 - RICARDO ALEXSANDRO DE MEDEIROS VALENTIM
Interno - 2524053 - ANTONIO LUIZ PEREIRA DE SIQUEIRA CAMPOS
Externo à Instituição - ANTONIO HIGOR FREIRE DE MORAIS - IFRN
Externo à Instituição - JOÃO PAULO QUEIROZ DOS SANTOS - IFRN
Externa à Instituição - LORENA ITATI PETRELLA - UC
Externo à Instituição - GUILHERME MEDEIROS MACHADO - UL
Externo à Instituição - CRISTINE MARTINS GOMES DE GUSMÃO - UFPE