RESUMEN
PURPOSE: In pediatric medicine, precise estimation of bone age is essential for skeletal maturity evaluation, growth disorder diagnosis, and therapeutic intervention planning. Conventional techniques for determining bone age depend on radiologists' subjective judgments, which may lead to non-negligible differences in the estimated bone age. This study proposes a deep learning-based model utilizing a fully connected convolutional neural network(CNN) to predict bone age from left-hand radiographs. METHODS: The data set used in this study, consisting of 473 patients, was retrospectively retrieved from the PACS (Picture Achieving and Communication System) of a single institution. We developed a fully connected CNN consisting of four convolutional blocks, three fully connected layers, and a single neuron as output. The model was trained and validated on 80% of the data using the mean-squared error as a cost function to minimize the difference between the predicted and reference bone age values through the Adam optimization algorithm. Data augmentation was applied to the training and validation sets yielded in doubling the data samples. The performance of the trained model was evaluated on a test data set (20%) using various metrics including, the mean absolute error (MAE), median absolute error (MedAE), root-mean-squared error (RMSE), and mean absolute percentage error (MAPE). The code of the developed model for predicting the bone age in this study is available publicly on GitHub at https://github.com/afiosman/deep-learning-based-bone-age-estimation . RESULTS: Experimental results demonstrate the sound capabilities of our model in predicting the bone age on the left-hand radiographs as in the majority of the cases, the predicted bone ages and reference bone ages are nearly close to each other with a calculated MAE of 2.3 [1.9, 2.7; 0.95 confidence level] years, MedAE of 2.1 years, RMAE of 3.0 [1.5, 4.5; 0.95 confidence level] years, and MAPE of 0.29 (29%) on the test data set. CONCLUSION: These findings highlight the usability of estimating the bone age from left-hand radiographs, helping radiologists to verify their own results considering the margin of error on the model. The performance of our proposed model could be improved with additional refining and validation.
Asunto(s)
Determinación de la Edad por el Esqueleto , Aprendizaje Profundo , Humanos , Estudios Retrospectivos , Determinación de la Edad por el Esqueleto/métodos , Niño , Femenino , Masculino , Arabia Saudita , Adolescente , Preescolar , Lactante , Redes Neurales de la Computación , Huesos de la Mano/diagnóstico por imagen , Huesos de la Mano/crecimiento & desarrolloRESUMEN
Recent evidence has linked certain mammographic characteristics, including breast calcifications (Bcs) and mammographic density (MD), with atherosclerotic cardiovascular disease risk factors in women, but data are limited and inconsistent. We aimed to evaluate the association of MD and/or Bcs with hypertension, diabetes, and hypercholesterolemia in women ≥40 years of age. Through hospital electronic records, we retrospectively identified mammograms of non-pregnant women aged ≥40 years and without breast cancer and retrieved reports and relevant data. MD and Bcs were recorded; risk factor status was diagnosed based on treatment profile and clinical and laboratory data. In total, 1406 women were included. MD was inversely related to hypertension, diabetes, hypercholesterolemia, triglyceride levels, age, and body mass index (BMI) (p value for trend <0.001). Bcs were positively associated with hypertension, diabetes, hypercholesterolemia, age, BMI, and elevated creatinine (p<0.05). Controlling for age and BMI, MD category A (MD-A) was independently associated with hypercholesterolemia; Bcs were independently associated with diabetes. Combining MD-A with Bcs did not increase the odds significantly. Analysis for additive interactions revealed a significant interaction between MD-A and BMI, increasing the odds of hypertension, and a trend for increased odds of diabetes by adding MD-A and/or Bcs to BMI. Decreased MD and presence of Bcs are associated with hypertension, diabetes, and hypercholesterolemia in women ≥40 years of age. MD-A may represent a new obesity index independently associated with hypercholesterolemia and additive to hypertension risk. Bcs are independently associated with diabetes. Combining MD and Bcs did not improve the odds significantly, which may reflect mechanistic differences.