Evolution of the Stethoscope: Advances with the Adoption of Machine Learning and Development of Wearable Devices / 결핵및호흡기질환

The stethoscope has long been used for the examination of patients, but the importance of auscultation has declined due to its several limitations and the development of other diagnostic tools. However, auscultation is still recognized as a primary diagnostic device because it is non-invasive and provides valuable information in real-time. To supplement the limitations of existing stethoscopes, digital stethoscopes with machine learning (ML) algorithms have been developed. Thus, now we can record and share respiratory sounds and artificial intelligence (AI)-assisted auscultation using ML algorithms distinguishes the type of sounds. Recently, the demands for remote care and non-face-to-face treatment diseases requiring isolation such as coronavirus disease 2019 (COVID-19) infection increased. To address these problems, wireless and wearable stethoscopes are being developed with the advances in battery technology and integrated sensors. This review provides the history of the stethoscope and classification of respiratory sounds, describes ML algorithms, and introduces new auscultation methods based on AI-assisted analysis and wireless or wearable stethoscopes.

Machine Learning Approaches for the Prediction of Prostate Cancer according to Age and the Prostate-Specific Antigen Level

PURPOSE: The aim of this study was to evaluate the applicability of machine learning methods that combine data on age and prostate-specific antigen (PSA) levels for predicting prostate cancer. MATERIALS AND METHODS: We analyzed 943 patients who underwent transrectal ultrasonography (TRUS)-guided prostate biopsy at Chungnam National University Hospital between 2014 and 2018 because of elevated PSA levels and/or abnormal digital rectal examination and/or TRUS findings. We retrospectively reviewed the patients’ medical records, analyzed the prediction rate of prostate cancer, and identified 20 feature importances that could be compared with biopsy results using 5 different algorithms, viz., logistic regression (LR), support vector machine, random forest (RF), extreme gradient boosting, and light gradient boosting machine. RESULTS: Overall, the cancer detection rate was 41.8%. In patients younger than 75 years and with a PSA level less than 20 ng/mL, the best prediction model for prostate cancer detection was RF among the machine learning methods based on LR analysis. The PSA density was the highest scored feature importances in the same patient group. CONCLUSIONS: These results suggest that the prediction rate of prostate cancer using machine learning methods not inferior to that using LR and that these methods may increase the detection rate for prostate cancer and reduce unnecessary prostate biopsy, as they take into consideration feature importances affecting the prediction rate for prostate cancer.

Expression of nitric oxide synthase isoforms in the porcine ovary during follicular development

The expression of nitric oxide synthase (NOS) isoforms in the ovaries of pigs was examined to study the involvement of nitric oxide, a product of NOS activity, in the function of the ovary. Western blot analysis detected three types of NOS in the ovary, including constitutive neuronal NOS (nNOS), endothelial NOS (eNOS) and inducible NOS (iNOS); eNOS immunoreactivity was more intense compared with that of iNOS or nNOS. Immunohistochemical studies demonstrated the presence of nNOS and eNOS in the surface epithelium, stroma, oocytes, thecal cells, and endothelial cells of blood vessels. Positive immunoreactions for nNOS and iNOS were detected in the granulosa cells from multilaminar and antral follicles, but not in those of unilaminar follicles. iNOS was detected in the surface epithelium, oocytes, and theca of multilaminar and antral follicles. Taking all of the findings into consideration, the observed differential expression of the three NOS isoforms in the ovary suggests a role for nitric oxide in modulating reproduction in pigs.