Steoporosis is a skeletal disorder that compromises bone resistance and its diagnosis is usually performed using dual energy X‐ray absorptiometry. Thus, the search for efficient diagnostic methods that do not involve the emission of ionizing radiation is necessary. This study proposed to use the Optical Coherence Tomography (OCT) to evaluate osteoporosis in alveolar bone. Osteoporosis lesions is simulated in vitro in porcine bones, and imaging is performed by OCT and micro‐computed tomography (micro‐CT). A developed algorithm is proposed to calculate the optical attenuation coefficient ( μ t), mean optical attenuation coefficient (μfalse¯t), integrated reflectivity (ΔR) and bone density ( BD). The μfalse¯t, ΔR and BD parameters shows a good correlation to micro‐CT parameters (bone volume/tissue volume and total porosity). The μ t and μfalse¯t methods are negatively impacted by non‐uniform intensities distribution in osteoporosis images. In conclusion, BD and ΔR analysis demonstrates to be potential techniques for diagnosis and monitoring of osteoporosis using OCT.

Breast cancer (BC) molecular subtypes diagnosis involves improving clinical uptake by Fourier transform infrared (FTIR) spectroscopic imaging, which is a non-destructive and powerful technique, enabling label free extraction of biochemical information towards prognostic stratification and evaluation of cell functionality. However, methods of measurements of samples demand a long time to achieve high quality images, making its clinical use impractical because of the data acquisition speed, poor signal to noise ratio, and deficiency of optimized computational framework procedures. To address those challenges, machine learning (ML) tools can facilitate obtaining an accurate classification of BC subtypes with high actionability and accuracy. Here, we propose a ML-algorithm-based method to distinguish computationally BC cell lines. The method is developed by coupling the K-neighbors classifier (KNN) with neighborhood components analysis (NCA), and hence, the NCA-KNN method enables to identify BC subtypes without increasing model size as well as adding additional computational parameters. By incorporating FTIR imaging data, we show that classification accuracy, specificity, and sensitivity improve, respectively, 97.5%, 96.3%, and 98.2%, even at very low co-added scans and short acquisition times. Moreover, a clear distinctive accuracy (up to 9 %) difference of our proposed method (NCA-KNN) was obtained in comparison with the second best supervised support vector machine model. Our results suggest a key diagnostic NCA-KNN method for BC subtypes classification that may translate to advancement of its consolidation in subtype-associated therapeutics.

Osteoporosis is a disease characterized by bone mineral density reduction, weakening the bone structure. Its diagnosis is performed using ionizing radiation, increasing health risk. Optical techniques are safer, due to non‐ionizing radiation use, but limited to the analyses of bone tissue. This limitation may be circumvented in the oral cavity. In this work we explored the use of laser speckle imaging (LSI) to differentiate the sound and osteoporotic maxilla and mandible bones in an in vitro model. Osteoporosis lesions were simulated with acid attack. The samples were evaluated by optical profilometry and LSI, using a custom software. Two image parameters were evaluated, speckle contrast ration and patches ratio. With the speckle contrast ratio, it was possible to differentiate sound from osteoporotic tissue. From speckle patches ratio it was observed a negative correlation with the roughness parameter. LSI is a promissory technique for assessment of osteoporosis lesions on alveolar bone.