Quantitative Screening of Cervical Cancers for Low-Resource Settings: Pilot Study of Smartphone-Based Endoscopic Visual Inspection After Acetic Acid Using Machine Learning Techniques

Background: Approximately 90% of global cervical cancer (CC) is mostly found in low- and middle-income countries. In most cases, CC can be detected early through routine screening programs, including a cytology-based test. However, it is logistically difficult to offer this program in low-resource settings due to limited resources and infrastructure, and few trained experts. A visual inspection following the application of acetic acid (VIA) has been widely promoted and is routinely recommended as a viable form of CC screening in resource-constrained countries. Digital images of the cervix have been acquired during VIA procedure with better quality assurance and visualization, leading to higher diagnostic accuracy and reduction of the variability of detection rate. However, a colposcope is bulky, expensive, electricity-dependent, and needs routine maintenance, and to confirm the grade of abnormality through its images, a specialist must be present. Recently, smartphone-based imaging systems have made a significant impact on the practice of medicine by offering a cost-effective, rapid, and noninvasive method of evaluation. Furthermore, computer-aided analyses, including image processing-based methods and machine learning techniques, have also shown great potential for a high impact on medicinal evaluations

Collective and static properties of model two-component plasmas

Classical MD data on the charge-charge dynamic structure factor of two-component plasmas (TCP) modeled in Phys. Rev. A 23, 2041 (1981) are analyzed using the sum rules and other exact relations. The convergent power moments of the imaginary part of the model system dielectric function are expressed in terms of its partial static structure factors, which are computed by the method of hypernetted chains using the Deutsch effective potential. High-frequency asymptotic behavior of the dielectric function is specified to include the effects of inverse bremsstrahlung. The agreement with the MD data is improved, and important statistical characteristics of the model TCP, such as the probability to find both electron and ion at one point, are determined.Comment: 25 pages, 6 figures, 5 tables. Published in Physical Review E http://link.aps.org/abstract/PRE/v76/e02640

Stopping of charged particles in dense one-component plasmas

Arkhipov, YV.; Askaruly, A.; Ashikbayeva, A.; Dubovtsev, D.; Syzganbayeva, S.; Tkachenko Gorski, IM. (2018). Stopping of charged particles in dense one-component plasmas. Recent Contribution to Physics. 65(2):51-57. http://hdl.handle.net/10251/133780S515765

Stopping of relativistic projectiles in two-component plasmas

Relativistic and correlation contributions to the polarizational energy losses of heavy projectiles moving in dense two-component plasmas are analyzed within the method of moments that allows one to reconstruct the Lindhard loss function from its three independently known power frequency moments. The techniques employed result in a thorough separation of the relativistic and correlation corrections to the classical asymptotic form for the polarizational losses obtained by Bethe and Larkin. The above corrections are studied numerically at different values of plasma parameters to show that the relativistic contribution enhances only slightly the corresponding value of the stopping power.This research was financially supported by the Spanish Ministerio de Educacion y Ciencia Project No. ENE2010-21116-C02-02 and by the Science Committee of the Ministry of Education and Sciences of the Republic of Kazakhstan under Grants No. 1128/GF, 1129/GF and 1099/GF. IMT acknowledges the hospitality of the al-Farabi Kazakh National University.Arkhipov, YV.; Ashikbayeva, AB.; Askaruly, A.; Davletov, AE.; Tkachenko Gorski, IM. (2013). Stopping of relativistic projectiles in two-component plasmas. EPL. 104(3):35003-p1-35003-p6. https://doi.org/10.1209/0295-5075/104/35003S35003-p135003-p6104

Deep Learning-Based Glaucoma Screening Using Regional RNFL Thickness in Fundus Photography

Since glaucoma is a progressive and irreversible optic neuropathy, accurate screening and/or early diagnosis is critical in preventing permanent vision loss. Recently, optical coherence tomography (OCT) has become an accurate diagnostic tool to observe and extract the thickness of the retinal nerve fiber layer (RNFL), which closely reflects the nerve damage caused by glaucoma. However, OCT is less accessible than fundus photography due to higher cost and expertise required for operation. Though widely used, fundus photography is effective for early glaucoma detection only when used by experts with extensive training. Here, we introduce a deep learning-based approach to predict the RNFL thickness around optic disc regions in fundus photography for glaucoma screening. The proposed deep learning model is based on a convolutional neural network (CNN) and utilizes images taken with fundus photography and with RNFL thickness measured with OCT for model training and validation. Using a dataset acquired from normal tension glaucoma (NTG) patients, the trained model can estimate RNFL thicknesses in 12 optic disc regions from fundus photos. Using intuitive thickness labels to identify localized damage of the optic nerve head and then estimating regional RNFL thicknesses from fundus images, we determine that screening for glaucoma could achieve 92% sensitivity and 86.9% specificity. Receiver operating characteristic (ROC) analysis results for specificity of 80% demonstrate that use of the localized mean over superior and inferior regions reaches 90.7% sensitivity, whereas 71.2% sensitivity is reached using the global RNFL thicknesses for specificity at 80%. This demonstrates that the new approach of using regional RNFL thicknesses in fundus images holds good promise as a potential screening technique for early stage of glaucoma

Collective phenomena in a quasi-classical electron fluid within the interpolational self-consistent method of moments

Collective processes in a quasi-classical electron gas are investigated within the framework of the interpolational self-consistent method of moments, which makes it possible to express the dispersion and decrement of plasma waves, and the dynamic structural factor of the system exclusively in terms of its static structural factor so that five sum rules are satisfied automatically. Different models are used of the static structure factor; the stability and robustness of the results of the moment approach taking into account the accuracy of these models is confirmed and tested by comparison to the alternative molecular dynamics simulation data

Advanced Ear Examination using Deep Learning-assisted Mobile Otoscope

Digital video otoscope is an indispensable tool in otology that allows inspection of the external auditory canal and tympanic membrane. However, existing solutions have limitations in the diagnosis of various ear diseases and portability. Here, we propose a mobile, deep learning-assisted otoscope for low-resource settings. Our deep learning architecture was trained on clinical data to identify and classify various ear diseases. To evaluate our platform, we compared its performance with the device used in the hospital practice. Our preliminary results demonstrated high diagnostic accuracy indicating a strong potential to become a viable screening solution in low-resource, non-specialist settings