BACKGROUND AND OBJECTIVES

The Philippines faces challenges in the screening of tuberculosis (TB), one of them being the shortage in the health workforce who are skilled and allowed to screen TB. Deep learning neural networks (DLNNs) have shown potential in the TB screening process utilizing chest radiographs (CXRs). However, local studies on AIbased TB screening are limited. This study evaluated qXR3.0 technology's diagnostic performance for TB screening in Filipino adults aged 15 and older. Specifically, we evaluated the specificity and sensitivity of qXR3.0 compared to radiologists' impressions and determined whether it meets the World Health Organization (WHO) standards.

A prospective cohort design was used to perform a study on comparing screening and diagnostic accuracies of qXR3.0 and two radiologist gradings in accordance with the Standards for Reporting Diagnostic Accuracy (STARD). Subjects from two clinics in Metro Manila which had qXR 3.0 seeking consultation at the time of study were invited to participate to have CXRs and sputum collected. Radiologists' and qXR3.0 readings and impressions were compared with respect to the reference standard Xpert MTB/RiF assay. Diagnostic accuracy measures were calculated.

With 82 participants, qXR3.0 demonstrated 100% sensitivity and 72.7% specificity with respect to the reference standard. There was a strong agreement between qXR3.0 and radiologists' readings as exhibited by the 0.7895 (between qXR 3.0 and CXRs read by at least one radiologist), 0.9362 (qXR 3.0 and CXRs read by both radiologists), and 0.9403 (qXR 3.0 and CXRs read as not suggestive of TB by at least one radiologist) concordance indices.

qXR3.0 demonstrated high sensitivity to identify presence of TB among patients, and meets the WHO standard of at least 70% specificity for detecting true TB infection. This shows an immense potential for the tool to supplement the shortage of radiologists for TB screening in the country. Future research directions may consider larger sample sizes to confirm these findings and explore the economic value of mainstream adoption of qXR 3.0 for TB screening.

INTRODUCTION: Ultrasonography (US) is the current standard of care for imaging surveillance in patients at risk of hepatocellular carcinoma (HCC). Magnetic resonance imaging (MRI) has been explored as an alternative, given the higher sensitivity of MRI, although this comes at a higher cost. We performed a cost-effective analysis comparing US and dual-sequence non-contrast-enhanced MRI (NCEMRI) for HCC surveillance in the local setting. METHODS: Cost-effectiveness analysis of no surveillance, US surveillance and NCEMRI surveillance was performed using Markov modelling and microsimulation. At-risk patient cohort was simulated and followed up for 40 years to estimate the patients' disease status, direct medical costs and effectiveness. Quality-adjusted life years (QALYs) and incremental cost-effectiveness ratio were calculated. RESULTS: Exactly 482,000 patients with an average age of 40 years were simulated and followed up for 40 years. The average total costs and QALYs for the three scenarios - no surveillance, US surveillance and NCEMRI surveillance - were SGD 1,193/7.460 QALYs, SGD 8,099/11.195 QALYs and SGD 9,720/11.366 QALYs, respectively. CONCLUSION Despite NCEMRI having a superior diagnostic accuracy, it is a less cost-effective strategy than US for HCC surveillance in the general at-risk population. Future local cost-effectiveness analyses should include stratifying surveillance methods with a variety of imaging techniques (US, NCEMRI, contrast-enhanced MRI) based on patients' risk profiles.
Humans ; Adult ; Carcinoma, Hepatocellular/diagnostic imaging* ; Liver Neoplasms/diagnostic imaging* ; Cost-Effectiveness Analysis ; Cost-Benefit Analysis ; Quality-Adjusted Life Years ; Magnetic Resonance Imaging/methods*

OBJECTIVE: To investigate a new noninvasive diagnostic model for nonalcoholic fatty liver disease (NAFLD) based on features of tongue images. METHODS: Healthy controls and volunteers confirmed to have NAFLD by liver ultrasound were recruited from China-Japan Friendship Hospital between September 2018 and May 2019, then the anthropometric indexes and sampled tongue images were measured. The tongue images were labeled by features, based on a brief protocol, without knowing any other clinical data, after a series of corrections and data cleaning. The algorithm was trained on images using labels and several anthropometric indexes for inputs, utilizing machine learning technology. Finally, a logistic regression algorithm and a decision tree model were constructed as 2 diagnostic models for NAFLD. RESULTS: A total of 720 subjects were enrolled in this study, including 432 patients with NAFLD and 288 healthy volunteers. Of them, 482 were randomly allocated into the training set and 238 into the validation set. The diagnostic model based on logistic regression exhibited excellent performance: in validation set, it achieved an accuracy of 86.98%, sensitivity of 91.43%, and specificity of 80.61%; with an area under the curve (AUC) of 0.93 [95% confidence interval (CI) 0.68-0.98]. The decision tree model achieved an accuracy of 81.09%, sensitivity of 91.43%, and specificity of 66.33%; with an AUC of 0.89 (95% CI 0.66-0.92) in validation set. CONCLUSIONS The features of tongue images were associated with NAFLD. Both the 2 diagnostic models, which would be convenient, noninvasive, lightweight, rapid, and inexpensive technical references for early screening, can accurately distinguish NAFLD and are worth further study.
Humans ; Non-alcoholic Fatty Liver Disease/diagnostic imaging* ; Ultrasonography ; Anthropometry ; Algorithms ; China

Hepatocellular carcinoma (HCC) is one of the most common malignancies and is a major cause of cancer-related mortalities worldwide (Forner et al., 2018; He et al., 2023). Sarcopenia is a syndrome characterized by an accelerated loss of skeletal muscle (SM) mass that may be age-related or the result of malnutrition in cancer patients (Cruz-Jentoft and Sayer, 2019). Preoperative sarcopenia in HCC patients treated with hepatectomy or liver transplantation is an independent risk factor for poor survival (Voron et al., 2015; van Vugt et al., 2016). Previous studies have used various criteria to define sarcopenia, including muscle area and density. However, the lack of standardized diagnostic methods for sarcopenia limits their clinical use. In 2018, the European Working Group on Sarcopenia in Older People (EWGSOP) renewed a consensus on the definition of sarcopenia: low muscle strength, loss of muscle quantity, and poor physical performance (Cruz-Jentoft et al., 2019). Radiological imaging-based measurement of muscle quantity or mass is most commonly used to evaluate the degree of sarcopenia. The gold standard is to measure the SM and/or psoas muscle (PM) area using abdominal computed tomography (CT) at the third lumbar vertebra (L3), as it is linearly correlated to whole-body SM mass (van Vugt et al., 2016). According to a "North American Expert Opinion Statement on Sarcopenia," SM index (SMI) is the preferred measure of sarcopenia (Carey et al., 2019). The variability between morphometric muscle indexes revealed that they have different clinical relevance and are generally not applicable to broader populations (Esser et al., 2019).
Humans ; Aged ; Sarcopenia/diagnostic imaging* ; Carcinoma, Hepatocellular/diagnostic imaging* ; Muscle, Skeletal/diagnostic imaging* ; Deep Learning ; Prognosis ; Radiomics ; Liver Neoplasms/diagnostic imaging* ; Retrospective Studies

Humans ; Myocarditis/diagnostic imaging* ; Magnetic Resonance Imaging ; Myocardium ; Giant Cells

Humans ; Myocarditis/diagnostic imaging* ; Magnetic Resonance Imaging ; Myocardium ; Giant Cells

Background and Objectives: The Philippines faces challenges in the screening of tuberculosis (TB), one of them being the shortage in the health workforce who are skilled and allowed to screen TB. Deep learning neural networks (DLNNs) have shown potential in the TB screening process utilizing chest radiographs (CXRs). However, local studies on AIbased TB screening are limited. This study evaluated qXR3.0 technology's diagnostic performance for TB screening in Filipino adults aged 15 and older. Specifically, we evaluated the specificity and sensitivity of qXR3.0 compared to radiologists' impressions and determined whether it meets the World Health Organization (WHO) standards. Methods: A prospective cohort design was used to perform a study on comparing screening and diagnostic accuracies of qXR3.0 and two radiologist gradings in accordance with the Standards for Reporting Diagnostic Accuracy (STARD). Subjects from two clinics in Metro Manila which had qXR 3.0 seeking consultation at the time of study were invited to participate to have CXRs and sputum collected. Radiologists' and qXR3.0 readings and impressions were compared with respect to the reference standard Xpert MTB/RiF assay. Diagnostic accuracy measures were calculated. Results: With 82 participants, qXR3.0 demonstrated 100% sensitivity and 72.7% specificity with respect to the reference standard. There was a strong agreement between qXR3.0 and radiologists' readings as exhibited by the 0.7895 (between qXR 3.0 and CXRs read by at least one radiologist), 0.9362 (qXR 3.0 and CXRs read by both radiologists), and 0.9403 (qXR 3.0 and CXRs read as not suggestive of TB by at least one radiologist) concordance indices. Conclusions qXR3.0 demonstrated high sensitivity to identify presence of TB among patients, and meets the WHO standard of at least 70% specificity for detecting true TB infection. This shows an immense potential for the tool to supplement the shortage of radiologists for TB screening in the country. Future research directions may consider larger sample sizes to confirm these findings and explore the economic value of mainstream adoption of qXR 3.0 for TB screening.
Tuberculosis ; Diagnostic Imaging ; Deep Learning

OBJECTIVE: To explore the brain effect mechanism and the correlation between brain functional imaging and cognitive function in treatment of depressive disorder (DD) with transcutaneous auricular vagus nerve stimulation (taVNS) based on the resting-state functional magenetic reasonance imaging (rs-fMRI). METHODS: Thirty-two DD patients were included in a depression group and 32 subjects of healthy condition were enrolled in a normal group. In the depression group, the taVNS was applied to bilateral Xin (CO₁₅) and Shen (CO₁₀), at disperse-dense wave, 4 Hz/20 Hz in frequency and current intensity ≤20 mA depending on patient's tolerance, 30 min each time, twice daily. The duration of treatment consisted of 8 weeks. The patients of two groups were undertaken rs-fMRI scanning. The scores of Hamilton depression scale (HAMD), Hamilton anxiety scale (HAMA) and Wisconsin card sorting test (WCST) were observed in the normal group at baseline and the depression group before and after treatment separately. The differential brain regions were observed before and after treatment in the two groups and the value of degree centrality (DC) of fMRI was obtained. Their correlation was analyzed in terms of HAMD, HAMA and WCST scores. RESULTS: The scores of HAMD and HAMA in the depression group were all higher than those in the normal group (P<0.05). After treatment, the scores of HAMD and HAMA were lower than those before treatment in the depression group; the scores of total responses, response errors and perseverative errors of WCST were all lower than those before treatment (P<0.05). The brain regions with significant differences included the left inferior temporal gyrus, the left cerebellar peduncles region 1, the left insula, the right putamen, the bilateral supplementary motor area and the right middle frontal gyrus. After treatment, the value of DC in left supplementary motor area was negatively correlated to HAMD and HAMA scores respectively (r=-0.324, P=0.012; r=-0.310, P=0.015); the value of DC in left cerebellar peduncles region 1 was negatively correlated to the total responses of WCST (r=-0.322, P=0.013), and the left insula was positively correlated to the total responses of WCST (r=0.271, P=0.036). CONCLUSION The taVNS can modulate the intensity of the functional activities of some brain regions so as to relieve depressive symptoms and improve cognitive function.
Humans ; Depression/therapy* ; Magnetic Resonance Imaging/methods* ; Vagus Nerve Stimulation/methods* ; Brain/diagnostic imaging* ; Transcutaneous Electric Nerve Stimulation/methods* ; Vagus Nerve

OBJECTIVE: To detect the body surface temperature of the relevant back-shu points in patients with chronic persistent asthma by infrared thermal imaging technology, and observe the specific changes of the body surface temperature of the relevant back-shu points under the condition of lung disease. METHODS: Forty-five patients with chronic persistent asthma (observation group) and 45 healthy subjects (control group) were selected. The body surface temperature of bilateral Feishu (BL 13), Geshu (BL 17), Pishu (BL 20) and Shenshu (BL 23) were measured by BK-MT02A medical infrared thermography. RESULTS: The body surface temperature of bilateral Feishu (BL 13), Geshu (BL 17), Pishu (BL 20) and Shenshu (BL 23) in the observation group was higher than that in the control group (P<0.01, P<0.05). The body surface temperature of bilateral Feishu (BL 13) and Geshu (BL 17) was higher than that of ipsilateral Pishu (BL 20) and Shenshu (BL 23) in the two groups (P<0.01, P<0.05). There was no statistically significant difference in body surface temperature between ipsilateral Feishu (BL 13) and Geshu (BL 17), between ipsilateral Pishu (BL 20) and Shenshu (BL 23) (P>0.05). CONCLUSION The pathological increase of body surface temperature of Feishu (BL 13), Geshu (BL 17), Pishu (BL 20) and Shenshu (BL 23) in patients with chronic persistent asthma indicates that above acupoints have specificity in reflecting lung diseases. The Feishu (BL 13) and Geshu (BL 17), which have significantly increased body surface temperature, not only provide objective basis for the pathological pathogenesis of "deficiency in origin and excess in symptom" in patients with chronic persistent asthma, but also reflect the different expressions of different acupoints on the same meridian for the lung diseases.
Humans ; Temperature ; Asthma/diagnostic imaging* ; Meridians ; Acupuncture Points ; Acupuncture Therapy/methods*

The fenrou zhijian is defined as potential gap between different layers in the three-dimensional network structure formed by the twelve meridian tendons. Various pathological changes of the meridian tendons lead to the adhesion and closure of fenrou zhijian, causing abnormal mechanical conduction of the meridian tendon system, which in turn leads to painful bi syndrome of meridian tendons. As such, restarting the fenrou zhijian is the key to acupuncture treatment for painful bi syndrome of meridian tendons. Under the guidance of musculoskeletal ultrasound, the level and the angle of needle insertion of acupuncture at fenrou zhijian could be accurately controlled, the efficacy of acupuncture is improved.
Humans ; Meridians ; Acupuncture Therapy ; Needles ; Pain ; Tendons/diagnostic imaging*