Machine learning and DSP algorithms for screening of possible osteoporosis using electronic stethoscopes

Osteoporosis is a prevalent but asymptomatic condition that affects a large population of the elderly, resulting in a high risk of fracture. Several methods have been developed and are available in general hospitals to indirectly assess the bone quality in terms of mineral material level and porosity. In this paper we describe a new method that uses a medical reflex hammer to exert testing stimuli, an electronic stethoscope to acquire impulse responses from tibia, and intelligent signal processing based on artificial neural network machine learning to determine the likelihood of osteoporosis. The proposed method makes decisions from the key components found in the time-frequency domain of impulse responses. Using two common pieces of clinical apparatus, this method might be suitable for the large population screening tests for the early diagnosis of osteoporosis, thus avoiding secondary complications. Following some discussions of the mechanism and procedure, this paper details the techniques of impulse response acquisition using a stethoscope and the subsequent signal processing and statistical machine learning algorithms for decision making. Pilot testing results achieved over 80% in detection sensitivity

Detection of osteoporosis from percussion responses using an electronic stethoscope and machine learning

Osteoporosis is an asymptomatic bone condition that affects a large proportion of the elderly population around the world, resulting in increased bone fragility and increased risk of fracture. Previous studies had shown that the vibroacoustic response of bone can indicate the quality of the bone condition. Therefore, the aim of the authors' project is to develop a new method to exploit this phenomenon to improve detection of osteoporosis in individuals. In this paper a method is described that uses a reflex hammer to exert testing stimuli on a patient's tibia and an electronic stethoscope to acquire the impulse responses. The signals are processed as mel frequency cepstrum coefficients and passed through an artificial neural network to determine the likelihood of osteoporosis from the tibia's impulse responses. Following some discussions of the mechanism and procedure, this paper details the signal acquisition using the stethoscope and the subsequent signal processing and the statistical machine learning algorithm. Pilot testing with 12 patients achieved over 80% sensitivity with a false positive rate below 30% and accuracies in the region of 70%. An extended dataset of 110 patients achieved an error rate of 30% with some room for improvement in the algorithm. By using common clinical apparatus and strategic machine learning, this method might be suitable as a large population screening test for the early diagnosis of osteoporosis, thus avoiding secondary complications

Targeted Intraoperative Radiotherapy Tumour Bed Boost during Breast-Conserving Surgery after Neoadjuvant Chemotherapy - a Subgroup Analysis of Hormone Receptor-Positive HER2-Negative Breast Cancer

INTRODUCTION: In a previous study our group showed a beneficial effect of targeted intraoperative radiotherapy (TARGIT-IORT) as an intraoperative boost on overall survival after neoadjuvant chemotherapy (NACT) compared to an external boost (EBRT). In this study we present the results of a detailed subgroup analysis of the hormone receptor (HR)-positive HER2-negative patients. METHODS: In this cohort study involving 46 patients with HR-positive HER2-negative breast cancer after NACT, we compared the outcomes of 21 patients who received an IORT boost to those of 25 patients treated with an EBRT boost. All patients received whole breast radiotherapy. RESULTS: Median follow-up was 49 months. Whereas disease-freesurvival and breast cancer-specific mortality were not significantly different between the groups, the 5-year Kaplan-Meier estimate of overall mortality was significantly lower by 21% with IORT, p = 0.028. Non-breast cancer-specific mortality was significantly lower by 16% with IORT, p = 0.047. CONCLUSION: Although our results have to be interpreted with caution, we have shown that the improved overall survival demonstrated previously could be reproduced in the HR-positive HER2-negative subgroup. These data give further support to the inclusion of such patients in the TARGIT-B (Boost) randomised trial that is testing whether IORT boost is superior to EBRT boost