OBJECTIVE: Research has shown that older people and smokers have a higher death probability from coronavirus disease 2019 (COVID-19). Thus, we investigated the effect of COVID-19 on death probability for individuals aged 65–70 years and smokers in India. MATERIALS AND METHODS: We did so using a differential learning (feed-backward) model. In the present study, we examined World Health Organization (WHO) declared COVID-19 data of India. We divided the patients into two groups accordingly: the population aged 65–70 years and female or male smokers. RESULTS: We observed that in the early stages of infection (up to 5 days), there was higher death probability in the older population; among smokers, it occurred in the middle period after infection (5–8 days). We estimated that the death probability among smokers was 1.905 times that of the older population. CONCLUSION: As Government of India, taking various initiatives to curb the spread of COVID-19, but these are not enough, so we suggest measures that should help to reduce COVID-19 infection in India.

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Recent technical advances have led to the application of artificial intelligence in many areas of medical science. This approach was applied early on to medical imaging, which involves a large amount of data for diagnosis. The application of artificial intelligence and imaging diagnostics for disease screening, diagnosis, and prognosis prediction is an area of active research. Early diagnosis and effective management of drug-resistant pulmonary tuberculosis (TB) can effectively control the spread of Mycobacterium TB, reduce hospitalization, and improve prognosis. We review the progress of artificial intelligence in assisting imaging-based diagnosis of this disease, and we offer useful perspectives on future research in this area.

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With the rapid development and progress of theory and technology, artificial intelligence (AI) has overcome many early technical limitations. Remarkable advances have recently been made in the application of AI to various areas of health care, and improvements in the performance of computer-aided diagnostics, such as accuracy, specificity, and processing speed, have been achieved, especially in the classification and identification of lesions. We review the various applications and worldwide progress of AI-based imaging diagnosis of infectious diseases.

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The spread of severe acute respiratory syndrome coronavirus 2 worldwide has created a major threat to human life and safety. Antiviral drugs and antibiotics have poor therapeutic effects, and there is no specific treatment for this virus. Chest computed tomography (CT) plays an important role in the diagnosis and management of coronavirus disease 2019 (COVID-19). We report a patient who was critically ill with COVID-19 and recovered after receiving transfusions of convalescent plasma. To evaluate the efficacy of convalescent plasma in the treatment of COVID-19, we compared chest CT findings, clinical manifestations, and laboratory findings before and after treatment with convalescent plasma. After the transfusion of convalescent plasma, clinical manifestations and indicators of inflammation improved, accompanied by an increase in the partial pressure of oxygen and oxygen saturation. Chest CT showed some resolution of the lung lesions, and multiple viral nucleic acid tests were negative. Therefore, the patient's condition was improved after the transfusion of convalescent plasma, suggesting that it may be an effective treatment for patients who are critically ill with COVID-19.

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Artificial intelligence (AI) is a cutting-edge technology that is applied in many fields. Medical imaging AI is also developing rapidly, and has markedly improved disease detection, diagnosis, prognosis, and monitoring. It also has led to fundamental changes in the way of radiologists' work. The potential new capabilities provided by AI will make the practice of radiology more efficient and effective. Herein, we review the application, current limitations and future opportunities of AI models in medical imaging.

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Pulmonary hydatid disease is a helminthic zoonotic disease caused by Echinococcus infection. The symptoms may appear several years after infection. Chest computed tomography (CT) is the preferred examination method and plays an important role in early diagnosis, treatment, and prognosis evaluation. CT can be used to diagnose simple cystic lesions. However, when the cysts are infected or ruptured, atypical imaging findings such as increased cyst density, blurring of the cyst wall, and surrounding exudation may lead to misdiagnosis of lung infection or lung abscess, hindering the therapeutic effect. We analyzed and compared the atypical imaging manifestations of pulmonary simple hydatid disease and hydatid cyst rupture. The aims of this report are to improve clinicians' understanding of these diseases, promote early diagnosis and treatment, and reduce the occurrence of complications.

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