Novel social distancing detector using local binary pattern in comparison with principal component analysis to improve accuracy

The objective is to detect Novel Social Distancing using Local Binary Pattern (LBP) in comparison with Principal Component Analysis (PCA). Social Distance deduction is performed using Local Binary Pattern(N=20) and Principal Component Analysis(N=20) algorithms. Google AI open Images dataset is used for image detection. Dataset contains more than 10,000 images. Accuracy of Principal Component Analysis is 89.8% and Local Binary Pattern is 93.9%. There exists a statistical significant difference between LBP and PCA with (p<0.05). Local Binary Pattern appears to perform significantly better than Principal Component Analysis for Social Distancing Detection. © 2023 Author(s).

Medical image-based detection of COVID-19 using Deep Convolution Neural Networks.

The demand for automatic detection of Novel Coronavirus or COVID-19 is increasing across the globe. The exponential rise in cases burdens healthcare facilities, and a vast amount of multimedia healthcare data is being explored to find a solution. This study presents a practical solution to detect COVID-19 from chest X-rays while distinguishing those from normal and impacted by Viral Pneumonia via Deep Convolution Neural Networks (CNN). In this study, three pre-trained CNN models (EfficientNetB0, VGG16, and InceptionV3) are evaluated through transfer learning. The rationale for selecting these specific models is their balance of accuracy and efficiency with fewer parameters suitable for mobile applications. The dataset used for the study is publicly available and compiled from different sources. This study uses deep learning techniques and performance metrics (accuracy, recall, specificity, precision, and F1 scores). The results show that the proposed approach produced a high-quality model, with an overall accuracy of 92.93%, COVID-19, a sensitivity of 94.79%. The work indicates a definite possibility to implement computer vision design to enable effective detection and screening measures.

A CNN-based scheme for COVID-19 detection with emergency services provisions using an optimal path planning.

Unmanned Air Vehicles (UAVs) are becoming popular in real-world scenarios due to current advances in sensor technology and hardware platform development. The applications of UAVs in the medical field are broad and may be shared worldwide. With the recent outbreak of COVID-19, fast diagnostic testing has become one of the challenges due to the lack of test kits. UAVs can help in tackling the COVID-19 by delivering medication to the hospital on time. In this paper, to detect the number of COVID-19 cases in a hospital, we propose a deep convolution neural architecture using transfer learning, classifying the patient into three categories as COVID-19 (positive) and normal (negative), and pneumonia based on given X-ray images. The proposed deep-learning architecture is compared with state-of-the-art models. The results show that the proposed model provides an accuracy of 94.92%. Further to offer time-bounded services to COVID-19 patients, we have proposed a scheme for delivering emergency kits to the hospitals in need using an optimal path planning approach for UAVs in the network.

Facilitating COVID recognition from X-rays with computer vision models and transfer learning.

Multimedia data plays an important role in medicine and healthcare since EHR (Electronic Health Records) entail complex images and videos for analyzing patient data. In this article, we hypothesize that transfer learning with computer vision can be adequately harnessed on such data, more specifically chest X-rays, to learn from a few images for assisting accurate, efficient recognition of COVID. While researchers have analyzed medical data (including COVID data) using computer vision models, the main contributions of our study entail the following. Firstly, we conduct transfer learning using a few images from publicly available big data on chest X-rays, suitably adapting computer vision models with data augmentation. Secondly, we aim to find the best fit models to solve this problem, adjusting the number of samples for training and validation to obtain the minimum number of samples with maximum accuracy. Thirdly, our results indicate that combining chest radiography with transfer learning has the potential to improve the accuracy and timeliness of radiological interpretations of COVID in a cost-effective manner. Finally, we outline applications of this work during COVID and its recovery phases with future issues for research and development. This research exemplifies the use of multimedia technology and machine learning in healthcare.

A mathematical model of SARS-CoV-2 immunity predicts paxlovid rebound.

Nirmatrelvir/ritonavir (Paxlovid), an oral antiviral medication targeting SARS-CoV-2, remains an important treatment for COVID-19. Initial studies of nirmatrelvir/ritonavir were performed in SARS-CoV-2 unvaccinated patients without prior confirmed SARS-CoV-2 infection; however, most individuals have now either been vaccinated and/or have experienced SARS-CoV-2 infection. After nirmatrelvir/ritonavir became widely available, reports surfaced of "Paxlovid rebound," a phenomenon in which symptoms (and SARS-CoV-2 test positivity) would initially resolve, but after finishing treatment, symptoms and test positivity would return. We used a previously described parsimonious mathematical model of immunity to SARS-CoV-2 infection to model the effect of nirmatrelvir/ritonavir treatment in unvaccinated and vaccinated patients. Model simulations show that viral rebound after treatment occurs only in vaccinated patients, while unvaccinated (SARS-COV-2 naïve) patients treated with nirmatrelvir/ritonavir do not experience any rebound in viral load. This work suggests that an approach combining parsimonious models of the immune system could be used to gain important insights in the context of emerging pathogens.

In Silico Structure-Based Vaccine Design.

Structure-based vaccine design (SBVD) is an important technique in computational vaccine design that uses structural information on a targeted protein to design novel vaccine candidates. This increasing ability to rapidly model structural information on proteins and antibodies has provided the scientific community with many new vaccine targets and novel opportunities for future vaccine discovery. This chapter provides a comprehensive overview of the status of in silico SBVD and discusses the current challenges and limitations. Key strategies in the field of SBVD are exemplified by a case study on design of COVID-19 vaccines targeting SARS-CoV-2 spike protein.

Comparison between original SARS-CoV-2 strain and omicron variant on thin-section chest CT imaging of COVID-19 pneumonia.

OBJECTIVES: We investigated different computed tomography (CT) features between Omicron-variant and original-strain SARS-CoV­2 pneumonia to facilitate the clinical management. MATERIALS AND METHODS: Medical records were retrospectively reviewed to select patients with original-strain SARS-CoV­2 pneumonia from February 22 to April 22, 2020, or Omicron-variant SARS-CoV­2 pneumonia from March 26 to May 31, 2022. Data on the demographics, comorbidities, symptoms, clinical types, and CT features were compared between the two groups. RESULTS: There were 62 and 78 patients with original-strain or Omicron-variant SARS-CoV­2 pneumonia, respectively. There were no differences between the two groups in terms of age, sex, clinical types, symptoms, and comorbidities. The main CT features differed between the two groups (p = 0.003). There were 37 (59.7%) and 20 (25.6%) patients with ground-glass opacities (GGO) in the original-strain and Omicron-variant pneumonia, respectively. A consolidation pattern was more frequently observed in the Omicron-variant than original-strain pneumonia (62.8% vs. 24.2%). There was no difference in crazy-paving pattern between the original-strain and Omicron-variant pneumonia (16.1% vs. 11.6%). Pleural effusion was observed more often in Omicron-variant pneumonia, while subpleural lesions were more common in the original-strain pneumonia. The CT score in the Omicron-variant group was higher than that in the original-strain group for critical-type (17.00, 16.00-18.00 vs. 16.00, 14.00-17.00, p = 0.031) and for severe-type (13.00, 12.00-14.00 vs 12.00, 10.75-13.00, p = 0.027) pneumonia. CONCLUSION: The main CT finding of the Omicron-variant SARS-CoV­2 pneumonia included consolidations and pleural effusion. By contrast, CT findings of original-strain SARS-CoV­2 pneumonia showed frequent GGO and subpleural lesions, but without pleural effusion. The CT scores were also higher in the critical and severe types of Omicron-variant than original-strain pneumonia.

Attitudes of psychiatrists toward telepsychiatry: A policy Delphi study.

Objectives: To delineate areas of consensus and disagreements among practicing psychiatrists from various levels of clinical experience, hierarchy and organizations, and to test their ability to converge toward agreement, which will enable better integration of telepsychiatry into mental health services. Methods: To study attitudes of Israeli public health psychiatrists, we utilized a policy Delphi method, during the early stages of the COVID pandemic. In-depth interviews were conducted and analyzed, and a questionnaire was generated. The questionnaire was disseminated amongst 49 psychiatrists, in two succeeding rounds, and areas of consensus and controversies were identified. Results: Psychiatrists showed an overall consensus regarding issues of economic and temporal advantages of telepsychiatry. However, the quality of diagnosis and treatment and the prospect of expanding the usage of telepsychiatry to normal circumstances-beyond situations of pandemic or emergency were disputed. Nonetheless, efficiency and willingness scales slightly improved during the 2nd round of the Delphi process. Prior experience with telepsychiatry had a strong impact on the attitude of psychiatrists, and those who were familiar with this practice were more favorable toward its usage in their clinic. Conclusions: We have delineated experience as a major impact on the attitudes toward telepsychiatry and the willingness for its assimilation in clinical practice as a legitimate and trustworthy method. We have also observed that the organizational affiliation significantly affected psychiatrists' attitude, when those working at local clinics were more positive toward telepsychiatry compared with employees of governmental institutions. This might be related to experience and differences in organizational environment. Taken together, we recommend to include hands-on training of telepsychiatry in medical education curriculum during residency, as well as refresher exercises for attending practitioners.

Automatic COVID-19 and Common-Acquired Pneumonia Diagnosis Using Chest CT Scans.

Even with over 80% of the population being vaccinated against COVID-19, the disease continues to claim victims. Therefore, it is crucial to have a secure Computer-Aided Diagnostic system that can assist in identifying COVID-19 and determining the necessary level of care. This is especially important in the Intensive Care Unit to monitor disease progression or regression in the fight against this epidemic. To accomplish this, we merged public datasets from the literature to train lung and lesion segmentation models with five different distributions. We then trained eight CNN models for COVID-19 and Common-Acquired Pneumonia classification. If the examination was classified as COVID-19, we quantified the lesions and assessed the severity of the full CT scan. To validate the system, we used Resnetxt101 Unet++ and Mobilenet Unet for lung and lesion segmentation, respectively, achieving accuracy of 98.05%, F1-score of 98.70%, precision of 98.7%, recall of 98.7%, and specificity of 96.05%. This was accomplished in just 19.70 s per full CT scan, with external validation on the SPGC dataset. Finally, when classifying these detected lesions, we used Densenet201 and achieved accuracy of 90.47%, F1-score of 93.85%, precision of 88.42%, recall of 100.0%, and specificity of 65.07%. The results demonstrate that our pipeline can correctly detect and segment lesions due to COVID-19 and Common-Acquired Pneumonia in CT scans. It can differentiate these two classes from normal exams, indicating that our system is efficient and effective in identifying the disease and assessing the severity of the condition.

Ergonomics in online education of medical undergraduates: A challenge to post-COVID transformation in educational activities.

BACKGROUND: Practicing incorrect postures in online and virtual education during the COVID-19 pandemic can cause significant study-related musculoskeletal problems among students. OBJECTIVE: This study evaluated the knowledge, attitude, and practice of sitting posture and computer ergonomics and study-related musculoskeletal problems in undergraduates who followed online education during the pandemic. METHODS: A cross-sectional online survey among a cohort of Sri Lankan medical undergraduates was conducted using a structured questionnaire with 56 multiple-choice or Yes/No type questions. RESULTS: Of the 410 participants, over 85% (n = 362) knew the correct posture to sit on the chair type that they frequently used for studies. However, the majority (n = 378,92.20%) practised incorrect sitting postures in which leaning forward (n = 319,77.80%) was the most common suboptimal posture. Knowledge (n = 161,40%) and practice (n = 167,40.73%) on taking frequent breaks were poor among the majority. Their knowledge on computer ergonomics was good (>80%, n = 304) except for the recommended eye-to-screen distance (n = 129,31.46%). Importantly, ∼50% (n = 206) did not practise the recommended eye-to-screen distance. Use of non-adjustable chairs with no armrests (n = 346,84.39%) and smartphones (n = 354,86.34%) were identified as the main factors which hindered correct practices. Study-related pain/discomfort reported by the majority (n = 241,58.78%) is potentially due to suboptimal ergonomics. Their attitude toward learning and practicing correct ergonomics in home workstations was good (n = 383,93.41%). CONCLUSION: Poor practice of posture and computer ergonomics, despite the good knowledge and attitude is possibly due to the suboptimal work environments. Introducing simple practical measures to facilitate ergonomically appropriate work environments is mandatory in virtual education to prevent study-related musculoskeletal problems.

Examining outcomes for service users accessing the Breaking Free Online computer-assisted therapy program for substance use disorders via a 'telehealth' approach: protocol for a two arm, parallel group randomized controlled trial.

BACKGROUND: Breaking Free Online (BFO), a computer-assisted therapy (CAT) program for substance use disorders (SUD), has been available across UK treatment services for the past decade and has demonstrated efficacy. The Covid-19 pandemic has contributed to digital and 'telehealth' approaches to healthcare delivery becoming more common and accepted, and has in parallel, increased numbers of referrals to SUD services because of the impact pandemic-related stress has had on substance using habits in the general population. Digital and telehealth approaches, such as BFO, have the potential to support the treatment system to meet this increased demand for SUD services. METHODS: Parallel-group randomized controlled trial of eight-week BFO as an adjunct to standard treatment for SUD, in comparison to standard treatment only, at a National Health Service (NHS) Mental Health Trust in North-West England. Participants will be service users aged 18 years and over with demonstrable SUD for at least 12-months. Interventional and control groups will be compared on multiple measures from baseline to post-treatment assessment at eight-weeks, and then three and six-months follow-up. Primary outcome will be self-reported substance use, with secondary outcomes being standardized assessments of substance dependence, mental health, biopsychosocial functioning and quality of life. DISCUSSION: This study will examine whether BFO and telehealth support, when delivered as an adjunct to standard SUD interventions, improves outcomes for services users receiving NHS SUD treatment. Findings from the study will be used to inform both developments to the BFO program and guidance around augmenting the delivery of CAT programs via telehealth. Trial registration registered with ISRCTN on 25th May 2021-registration number: 13694016. PROTOCOL VERSION: 3.0 05th April 2022. TRIAL STATUS: This trial is currently open to recruitment-estimated to be completed in May 2023.

MonDiaL-CAD: Monkeypox diagnosis via selected hybrid CNNs unified with feature selection and ensemble learning.

Objective: Recently, monkeypox virus is slowly evolving and there are fears it will spread as COVID-19. Computer-aided diagnosis (CAD) based on deep learning approaches especially convolutional neural network (CNN) can assist in the rapid determination of reported incidents. The current CADs were mostly based on an individual CNN. Few CADs employed multiple CNNs but did not investigate which combination of CNNs has a greater impact on the performance. Furthermore, they relied on only spatial information of deep features to train their models. This study aims to construct a CAD tool named "Monkey-CAD" that can address the previous limitations and automatically diagnose monkeypox rapidly and accurately. Methods: Monkey-CAD extracts features from eight CNNs and then examines the best possible combination of deep features that influence classification. It employs discrete wavelet transform (DWT) to merge features which diminishes fused features' size and provides a time-frequency demonstration. These deep features' sizes are then further reduced via an entropy-based feature selection approach. These reduced fused features are finally used to deliver a better representation of the input features and feed three ensemble classifiers. Results: Two freely accessible datasets called Monkeypox skin image (MSID) and Monkeypox skin lesion (MSLD) are employed in this study. Monkey-CAD could discriminate among cases with and without Monkeypox achieving an accuracy of 97.1% for MSID and 98.7% for MSLD datasets respectively. Conclusions: Such promising results demonstrate that the Monkey-CAD can be employed to assist health practitioners. They also verify that fusing deep features from selected CNNs can boost performance.

Research on the classification of CT images of COVID-19 by pre-training model based on self-attention mechanism

Nowadays, with the outbreak of COVID-19, the prevention and treatment of COVID-19 has gradually become the focus of social disease prevention, and most patients are also more concerned about the symptoms. COVID-19 has symptoms similar to the common cold, and it cannot be diagnosed based on the symptoms shown by the patient, so it is necessary to observe medical images of the lungs to finally determine whether they are COVID-19 positive. As the number of patients with symptoms similar to pneumonia increases, more and more medical images of the lungs need to be generated. At the same time, the number of physicians at this stage is far from meeting the needs of patients, resulting in patients unable to detect and understand their own conditions in time. In this regard, we have performed image augmentation, data cleaning, and designed a deep learning classification network based on the data set of COVID-19 lung medical images. accurate classification judgment. The network can achieve 95.76% classification accuracy for this task through a new fine-tuning method and hyperparameter tuning we designed, which has higher accuracy and less training time than the classic convolutional neural network model. © 2023 SPIE.

Older Adults Using Technology for Meaningful Activities During COVID-19: An Analysis Through the Lens of Self-Determination Theory

Restrictions during the COVID-19 pandemic significantly affected people's opportunities to engage in activities that are meaningful to their lives. In response to these constraints, many people, including older adults, turned to digital technologies as alternative ways to pursue meaningful activities. These technology-mediated activities, however, presented new challenges for older adults' everyday use of technology. In this paper, we investigate how older adults used digital technologies for meaningful activities during COVID-19 restrictions. We conducted in-depth interviews with 40 older adults and analyzed the interview data through the lens of self-determination theory (SDT). Our analysis shows that using digital technologies for meaningful activities can both support and undermine older people's three basic psychological needs for autonomy, competence, and relatedness. We argue that future technologies should be designed to empower older adults' content creation, engagement in personal interests, exploration of technology, effortful communication, and participation in beneficent activities. © 2023 ACM.

Breaking the Ice with Group Flow: A Collaborative VR Serious Game for Relationship Enhancement

It is usually hard for unfamiliar partners to rapidly 'break the ice' in the early stage of relationship establishment, which hinders the development of relationship and even affects the team productivity. To solve this problem, we proposed a collaborative serious game for icebreaking by combining immersive virtual reality (VR) with brain-computer interface based on the team flow framework. We designed a multiplayer collaboration task with the theme of fighting COVID-19 and proposed an approach to improve empathy between team members by sharing their real-time mental state in VR;in addition, we propose an EEG-based method for dynamic evaluation and enhancement of group flow experience to achieve better team collaboration. Then, we developed a prototype system and performed a user study. Results show that our method has good ease of use and can significantly reduce the psychological distance among team members. Especially for unfamiliar partners, both functions of mental state sharing and group flow regulation enhancement can significantly reduce the psychological distance. © 2023 IEEE.

Spatial Interactions and the Spread of COVID-19: A Network Perspective

We use unique data on the travel history of confirmed patients at a daily frequency across 31 provinces in China to study how spatial interactions influence the geographic spread of pandemic COVID-19. We develop and simultaneously estimate a structural model of dynamic disease transmission network formation and spatial interaction. This allows us to understand what externalities the disease risk associated with a single place may create for the entire country. We find a positive and significant spatial interaction effect that strongly influences the duration and severity of pandemic COVID-19. And there exists heterogeneity in this interaction effect: the spatial spillover effect from the source province is significantly higher than from other provinces. Further counterfactual policy analysis shows that targeting the key province can improve the effectiveness of policy interventions for containing the geographic spread of pandemic COVID-19, and the effect of such targeted policy decreases with an increase in the time of delay.

COVID-19: comparison lung CT signs and biochemical parameters in the groups of patients with three-time positive RT-PCR and with triple negative RT-PCR test during the period of hospitalization.

Research goal. Comparative characteristics of the dynamics of CT semiotics and biochemical parameters of two groups of patients: with positive RT-PCR and with triple negative RT-PCR. Reflection of the results by comparing them with the data already available in the literature. The aim of the study is to compare the dynamics of CT semiotics and biochemical parameters of blood tests in two groups of patients: with positive RT-PCR and with triple negative RT-PCR. We also reflect the results by comparing them with the data already available in the literature. Materials and methods. We have performed a retrospective analysis of CT images of 66 patients: group I (n1 = 33) consists of patients who had three- time negative RT-PCR (nasopharyngeal swab for SARS-CoV-2 RNA) during hospitalization, and group II (n2 = 33) includes patients with triple positive RT-PCR. An important selection criterion is the presence of three CT examinations (primary, 1st CT and two dynamic examinations - 2nd CT and 3rd CT) and at least two results of biochemistry (C-reactive protein (CRP), fibrinogen, prothrombin time, procalcitonin) performed in a single time interval of +/- 5 days from 1st CT, upon admission, and +/- 5 days from 3st CT. A total of 198 CT examinations of the lungs were analyzed (3 examinations per patient). Results. The average age of patients in the first group was 58 +/- 14.4 years, in the second - 64.9 +/- 15.7 years. The number of days from the moment of illness to the primary CT scan 6.21 +/- 3.74 in group I, 7.0 (5.0-8.0) in group II, until the 2nd CT scan - 12.5 +/- 4, 87 and 12.0 (10.0-15.0), before the 3rd CT scan - 22.0 (19.0-26.0) and 22.0 (16.0-26.0), respectively. In both groups, all 66 patients (100%), the primary study identified the double-sided ground-glass opacity symptom and 36 of 66 (55%) patients showed consolidation of the lung tissue. Later on, a first follow-up CT defined GGO not in all the cases: it was presented in 22 of 33 (67%) patients with negative RT-PCR (group I) and in 28 of 33 (85%) patients with the positive one (group II). The percentage of studies showing consolidation increased significantly: up to 30 of 33 (91%) patients in group I, and up to 32 of 33 (97%) patients in group II. For the first time, radiological symptoms of "involutional changes" appeared: in 17 (52%) patients of the first group and in 5 (15%) patients of the second one. On second follow-up CT, GGO and consolidations were detected less often than on previous CT: in 1 and 27 patients of group I (3% and 82%, respectively) and in 6 and 30 patients of group II (18% and 91%, respectively), although the consolidation symptom still prevailed significantly . The peak of "involutional changes" occurred on last CT: 31 (94%) and 25 (76%) patients of groups I and II, respectively.So, in the groups studied, the dynamics of changes in lung CT were almost equal. After analyzing the biochemistry parameters, we found out that CRP significantly decreased in 93% of patients (p < 0.001) in group I;in group II, there was a statistically significant decrease in the values of C-reactive protein in 81% of patients (p = 0.005). With an increase in CT severity of coronavirus infection by one degree, an increase in CRP by 41.8 mg/ml should be expected. In group I, a statistically significant (p = 0.001) decrease in fibrinogen was recorded in 77% of patients;and a similar dynamic of this indicator was observed in group II: fibrinogen values decreased in 66% of patients (p = 0.002). Such parameters as procalcitonin and prothrombin time did not significantly change during inpatient treatment of the patients of the studied groups (p = 0.879 and p = 0.135), which may indicate that it is inappropriate to use these parameters in assessing dynamics of patients with a similar course of the disease. When comparing the outcomes of the studied groups, there was a statistically significant higher mortality in group II - 30.3%, in group I - 21.2% (p = 0.043). Conclusion. According to our data, a course of the disease does not significantly differ in the groups o patients with positive RT-PCR and three-time negative RT-PCR. A negative RT-PCR analysis may be associated with an individual peculiarity of a patient such as a low viral load of SARS-CoV-2 in the upper respiratory tract. Therefore, with repeated negative results on the RNA of the virus in the oro- and nasopharynx, one should take into account the clinic, the X-ray picture and biochemical indicators in dynamics and not be afraid to make a diagnosis of COVID-19.Copyright © 2021 ALIES. All rights reserved.

Could platelet mass index (PMI) be a new prognostic biomarker for COVID-19?

Aim: Although most patients with COVID-19 experience respiratory tract infections, severe reactions to the virus may cause coagulation abnormalities that mimic other systemic coagulopathies associated with severe infections, such as disseminated intravascular coagulation and thrombotic microangiopathy. Fluctuations in platelet markers, which are an indicator of the acute phase response for COVID-19, are of clinical importance. The aim of this study is to evaluate the relationship between disease severity and Platelet Mass Index (MPI) parameters in COVID-19 patients. Material(s) and Method(s): This retrospective observational study was conducted with patients who were diagnosed with COVID-19 in a tertiary hospital. The study was continued with the remaining 280 patients. All laboratory data were scanned retrospectively from patient files and hospital information system. Result(s): A very high positive correlation was found between PMI and PLT. The PMI value in women was significantly higher than in men. It was observed that PMI did not differ significantly in terms of mortality, intubation, CPAP and comorbidity. PMI vs. Pneumonia Ct Severity Score, biochemistry parameters (AST, CRP), hemogram parameters (WBC, HGB, HCT, MCV, LYM, MPV EO) and coagulation factors (aPTT and FIB) at various levels of positive/negative, weak and strong, and significant relationship was found. There was no significant relationship between hormone and D-dimer when compared with PMI. Discussion(s): Although platelet count alone does not provide information about the prognosis of the disease, PMI may guide the clinician as an indicator of lung damage in seriously ill patients.Copyright © 2022, Derman Medical Publishing. All rights reserved.

Examination of Variables Affecting the Perceptions of Academic Performance of Higher Education Students during the Distance Education Process

COVID-19 has had serious consequences in all areas of social life, including education. In this period, distance education appeared as an inevitable solution. Even today, when the pandemic process is over and re-normalization has begun, online teaching environments have become such an indispensable part of education systems that it has been decided that a certain proportion of the courses will be conducted online in universities. For this reason, determining student experiences in online courses is important in planning the future of distance education. Since academic performance is the output of the teaching process, students' academic performance is one of the topics of interest in higher education research. There may be different factors affecting the academic performance of students in the distance education process, which imposes more responsibility on students and requires self-control. This study aimed to examine the relationship of academic performance in the distance education with home infrastructure, student interaction, computer skills, academic satisfaction. This research is based on a large-scale study, "The impact of the COVID-19 pandemic on the lives of higher education students", examining the pandemic's impact on higher education student perceptions in 2020. It has been observed that home infrastructure has a significant impact on the student's academic performance. The infrastructure increases the interaction of the student. When home infrastructure is taken as a control variable, students' computer skills are the highest predictor of their perception of academic performance, followed by their online interactions and, finally, perceived satisfaction. Today, pandemic conditions are still ongoing. In addition, even as the pandemic ends, online education has become an indispensable part of our education system. Therefore, the findings of the research would be beneficial for the ongoing planning process.