Journal of Radiation Medicine in the Tropics

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Background: The world is remodeling with the emergence of the most revolutionary technological advancement called artificial intelligence (AI). The study is to evaluate the knowledge, practice, and perception of AI in healthcare among care providers. Methodology: A descriptive, prospective design was adopted over a period of 8 months using a well-structured, self-administered questionnaire administered to medical practitioners. A total of 510 questionnaires out of 525 administered were retrieved immediately after completion. The responses were collated and analyzed using SPSS version 21.0 in line with the study objectives. The data were presented orderly in frequency tables and charts. Results: Male constituted 59.02%, while the female was 40.98%. The mean (±standard deviation) age of the respondents is 45.88 ± 10.26 years. 21.18% of the respondents are consultants, 42.35% are resident doctors while 9.22% are medical officers and 27.25% constituting interns. 94.31% of the respondents know about AI, and the internet has been the main source (89.32%). Majorities know that AI is applied in radiology (96.50%) and surgery (94.05%). Four hundred and thirty-seven (91.81%) responded that there was no form of AI in their facilities and are willing to be trained. Two hundred and sixty-eight (55.83%) agreed that AI would improve healthcare services, while 43.66% of the respondents thought that it will eliminate the human factor. Poor knowledge of information technology, absence of legislation, and promotion of self-medication were the anticipated challenges. Conclusion: Little is known about AI use in pathology, drug-dispensing, and nursing care. There is no AI practice in this environment, but physicians are willing to embrace AI. The fear of eliminating the human factor and cause job loss remained a concern. Training and legislation to regulate AI use are recommended to improve the knowledge, practice, and perception of AI among medical personnel in the developing world.

Background: COVID-19, a zoonotic disease caused by severe acute respiratory syndrome coronavirus-2, is responsible for the pandemic of 2020. Despite the availability and usefulness of chest radiographs (CXR) in diagnosis and monitoring of suspected as well as infected patients with COVID-19, there is paucity of studies about imaging findings in COVID-19 from Sub-Saharan Africa including Nigeria. Aim: This study aims to describe the findings and correlates of chest radiographic appearances in COVID-19 pneumonia at Kano, Nigeria. Materials and Methods: This was a retrospective study conducted at radiology units of four radiology diagnostic facilities that were very active during the peak period of the pandemic in Kano metropolis. All patients with confirmed COVID-19 infection and also underwent chest radiographic examinations at these facilities were included. The findings considered in the evaluation were the presence or absence and the type of pulmonary or pleural lesions/abnormality, and their distribution; including their association with demographics and clinical presentations. Statistical analysis was performed using SPSS version 23.0. Results: Of 125 patients, 98 (78.4%) were male. The mean age was 57.3 ± 12 years. One hundred and one (80.8%) of them had abnormal chest radiographic findings. These patients showed significant higher occurrences of anosmia than those with normal CXRs (P < 0.05). The occurrence of ground-glass opacity in both lower zones was significantly seen in 43 (62.3%) patients between 40 and 60 years and in 40 (85%) of those older than 60. Anosmia and age were respectively the only symptoms that independently predict if the COVID-19 patient may likely present with the abnormal CXRs or not (0.175 [0.057–0.542] P = 0.002; 3.7 [1.423–9.622] P = 0.007). Conclusions: Majority of our patients presents with abnormalities on CXRs. Older age, anosmia were the major predictors of chest radiographic abnormalities, which were mainly bilateral asymmetric lower zones ground glass opacities.

Introduction: Radiology request form (RRF) is a medical referral to a radiologist requesting input concerning the diagnosis, treatment, follow-up of patients, and intervention where necessary. The RRF usually provides information on the patient(s) and the investigation(s) being requested. Methodology: This was a cross-sectional descriptive study, in which request forms were retrospectively reviewed over a period of 6 months in the Radiology Department of the Rivers State University Teaching Hospital. A total of 1131 request forms filled by medical doctors were analyzed to ascertain the degree of completeness of filling the forms. The data were collated and analyzed using the IBM Statistical Package for the Social Sciences version 23. The results were expressed in simple percentages, frequencies, and presented in tables, charts, and graphs. Results: Ultrasonography was the most requested radiologic investigation (55.4%), while X-ray and computed tomography scan account for 41.4% and 1.1%, respectively. Majority of the forms had adequately entered date of request (97.7%) and required investigation and patient's names (99.6%). Patient's age was appropriately entered in 51.9% of cases, while 34.1% kept the space blank. Clinical information was indicated in 50.8% of the forms, while 97.2% did not include history of patient's allergy as regards the use of radiopharmaceutical agents. The patient's consultant and resident's doctor names were appropriately entered in 72.1% and 81.7% of the request forms, respectively. Signature authentication by the resident doctor is available in 13.2% of the request forms. Conclusion: It was observed that majority of the RRFs were not completely and appropriately filled; thus, complete information regarding the patient is not provided to the radiologist to enhance report accuracy. Clinical information if adequately provided can improve the quality of patient management, follow-up, training, and research.

Online since 21^st April 2020