Articles: ninos.
-
Comparative Study
Prediction of age and sex from paranasal sinus images using a deep learning network.
This study was conducted to develop a convolutional neural network (CNN)-based model to predict the sex and age of patients by identifying unique unknown features from paranasal sinus (PNS) X-ray images. We employed a retrospective study design and used anonymized patient imaging data. Two CNN models, adopting ResNet-152 and DenseNet-169 architectures, were trained to predict sex and age groups (20-39, 40-59, 60+ years). ⋯ CAM suggested that the maxillary sinus and the periodontal area were primary factors in identifying age groups. Our deep learning model could predict sex and age based on PNS X-ray images. Therefore, it can assist in reducing the risk of patient misidentification in clinics.
-
Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is a simple, reliable, minimally invasive and effective procedure. However, a surgical technique may be required, if the results are negative. Therefore, there is a need for new studies to increase the diagnostic value of EBUS-TBNA and provide additional information to guide the biopsy in performing the procedure. Here, we aimed to investigate the diagnostic value of EBUS-TBNA and 18-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) in diagnosis of hilar and/or mediastinal lymph nodes (LNs). It was also aimed to determine the contributions of real-time ultrasonography (USG) images of LNs to distinguishing between the malignant and benign LNs during EBUS-TBNA, and in the diagnosis of anthracotic LNs. ⋯ EBUS can contribute to the differential diagnosis of malignant, anthracotic and other benign LNs. Such contributions can guide clinician bronchoscopists during EBUS-TBNA. The triple modality of EBUS-TBNA, 18FDG PET/CT, and USG may increase the diagnostic value in hilar and mediastinal lymphadenopathies.
-
Despite the establishment of the links between ulcerative colitis (UC) and depression, between UC and gut microbiota, few correlations between depression and gut microbiota have yet been demonstrated especially in ulcerative colitis patients. The objective of our study was therefore to determine whether the comorbidity of depressive disorder in ulcerative colitis patients correlate with alterations in the gut microbiota and to identify the specific microbiota signatures associated with depression. Between March 2017 and February 2018, 31 healthy volunteers, 31 UC patients without depression, and 31 UC patients with depression from Longhua Hospital were enrolled. ⋯ The UC with depression group had the lowest microbial abundance. With regard to the vital bacteria in the microbiota-gut-brain axis, patients with UC and depression had the lowest abundance of Firmicutes, Clostridia, and Clostridiales but the highest abundance of Proteobacteria, Gammaproteobacteria, and Bacilli. The presence of depression in UC patients presented significant differences in the composition of gut microbiota compared with UC patients without depression, with increased abundance of Firmicutes and reduced abundance of Proteobacteria.
-
Comparative Study
A machine-learning based approach to quantify fine crackles in the diagnosis of interstitial pneumonia: A proof-of-concept study.
Fine crackles are frequently heard in patients with interstitial lung diseases (ILDs) and are known as the sensitive indicator for ILDs, although the objective method for analyzing respiratory sounds including fine crackles is not clinically available. We have previously developed a machine-learning-based algorithm which can promptly analyze and quantify the respiratory sounds including fine crackles. In the present proof-of-concept study, we assessed the usefulness of fine crackles quantified by this algorithm in the diagnosis of ILDs. ⋯ In addition, the increased mean FCQV was associated with the presence of traction bronchiectasis (P = .003) and honeycombing (P = .004) in HRCT. Furthermore, in discriminating ILDs in HRCT, an FCQV-based determination of the presence or absence of fine crackles indicated a higher sensitivity compared to a chest X-ray-based determination of the presence or absence of ILDs. We herein report that the machine-learning-based quantification of fine crackles can predict the HRCT findings of lung fibrosis and can support the prompt and sensitive diagnosis of ILDs.
-
To analyze the effects of orthognathic surgery on stress distributions in the temporomandibular joint (TMJ) of patients with jaw deformity during unilateral molar clenching (UMC) by using three-dimensional (3D) finite element method. Nine patients with jaw deformity (preoperative group, 26.1 ± 5.6 years old) and 9 asymptomatic subjects (control group, 22.0 ± 6.0 years old) were selected. Furthermore, the patients with jaw deformity were also considered as the postoperative group after undergoing orthognathic surgery. ⋯ The stresses on both TMJs of the control group were significantly different, whereas there was no significant difference on both sides for the preoperative group. All the stresses of the preoperative group were greater than those of the control and postoperative groups, except the minimum principal stress on the ipsilateral fossa. Orthognathic surgery is beneficial for alleviating the abnormal stress distributions on TMJ.