Gender and Age Differences in Cardiac Size Parameters of Ghanaian Adults: Can One Parameter Fit All? Part Two

BACKGROUND፡ The cardiothoracic ratio (CTR) is a radiographic parameter commonly used in assessing the size of the heart. This study evaluated the gender and age-based differences in the average cardiothoracic ratios, and transverse cardiac diameters (TCD) of adults in Ghana.METHOD: Plain chest radiography reports of 2004 patients (without known chest related diseases) generated by two radiologists with at least 15 years’ experience from July 2016 to June 2020 were retrospectively analyzed for this study. The CTR for each radiograph was calculated using the formula CTR (TCD÷TTD)×100, where TCD and TTD represent transverse cardiac diameters and transverse thoracic diameters, respectively. Data were analyzed with the statistical package for social sciences version 23. The independent t-test and One-way Analysis of Variance tests were used in the analyses.RESULTS: A total of 2004 patients’ chest x-rays were used in the analyses. The ages of the patients ranged from 20-86 years old with a mean of 39.4±14.04 years. The mean CTR for males was 46.6 ± 3.7% while that of females was 47.7±3.7%. The difference in the overall CTR among the gender groupings was statisticallysignificant (p = 0.001). There were statistically significant differences between the gender categories among patients in the following age groups: 30-39 (p=0.046), 40-49 (p=0.001), 50-59 (p=0.001) and 60-69 (p=0.001).CONCLUSION: The study reveals there are significant gender and age-related differences in cardiac size parameters obtainedfrom routine, frontal chest radiographs. These differences, if considered, may result in early and appropriate treatment of cardiac pathology in some age groups

Generalisability of deep learning models in low-resource imaging settings: A fetal ultrasound study in 5 African countries

Most artificial intelligence (AI) research and innovations have concentrated in high-income countries, where imaging data, IT infrastructures and clinical expertise are plentiful. However, slower progress has been made in limited-resource environments where medical imaging is needed. For example, in Sub-Saharan Africa the rate of perinatal mortality is very high due to limited access to antenatal screening. In these countries, AI models could be implemented to help clinicians acquire fetal ultrasound planes for diagnosis of fetal abnormalities. So far, deep learning models have been proposed to identify standard fetal planes, but there is no evidence of their ability to generalise in centres with low resources, i.e. with limited access to high-end ultrasound equipment and ultrasound data. This work investigates for the first time different strategies to reduce the domain-shift effect arisen from a fetal plane classification model trained on one clinical centre with high-resource settings and transferred to a new centre with low-resource settings. To that end, a classifier trained with 1,792 patients from Spain is first evaluated on a new centre in Denmark in optimal conditions with 1,008 patients and is later optimised to reach the same performance in five African centres (Egypt, Algeria, Uganda, Ghana and Malawi) with 25 patients each. The results show that a transfer learning approach for domain adaptation can be a solution to integrate small-size African samples with existing large-scale databases in developed countries. In particular, the model can be re-aligned and optimised to boost the performance on African populations by increasing the recall to 0.92±0.04 and at the same time maintaining a high precision across centres. This framework shows promise for building new AI models generalisable across clinical centres with limited data acquired in challenging and heterogeneous conditions and calls for further research to develop new solutions for usability of AI in countries with less resources and, consequently, in higher need of clinical support

Audit of the appropriateness of the indication for obstetric sonography in a tertiary facility in Ghana

Introduction: the use of ultrasound is one of the most vital tools in the management of pregnancies and contributes significantly in improving maternal and child health. Certain indications in pregnancy, guide the obstetrician as to which obstetric scan deems appropriate. The full realization of the benefits of ultrasound depends on whether it is being used appropriately or not, and hence this study aimed at auditing for the appropriate indications for obstetric ultrasound. Methods: a review of all request forms for obstetric scan between June 2019 and July 2020 was performed to assess the appropriateness of requests for obstetric ultrasound at the Cape Coast Teaching Hospital. The data obtained was analyzed using SPSS (SPSS Inc. Chicago, IL version 20.0). A Chi-squared test of independence was used to check for statistically significant differences between variables at p ≤ 0.05. Results: three hundred and fourteen (314) out of the 527 request forms had clinical indications stated. 174 (81.7%) of requests from Cape Coast Teaching Hospital and 39 (18.3%) from other health centers did not indicate patients clinical history/indication on the request forms. Majority 76 (68.5%) of scans in the first trimester were done without indications/history. Only 29 of requests with clinical history were inappropriate. Conclusion: practitioners should be mindful of adequately completing request forms for obstetric investigations since e a large number of practitioners do not state the history/indications for the scans. There should be continuous medical education on the importance of appropriate indication for obstetric ultrasound

Radiologic evaluation of orbital index among Ghanaians using CT scan

Abstract Background Orbital index (OI) expresses the proportion of the orbital height to the orbital width and varies with race, regions within the same race and periods in evolution. This index is useful in forensic medicine, anthropology and surgery. However, the average OI among Ghanaian adults was unknown. Aim The aim of this study was to determine the orbital index of adult Ghanaians and classify them under one of the three predetermined groups. Method The study design was a retrospective cross-sectional. A systematic random sampling method was used for selecting 350 adult Ghanaian head computed tomography images available from 1 January to 31 December 2015 at KBTH Hospital. The orbital height and orbital width of each orbit were measured on a 3D CT skull. Data was analysed using Microsoft Excel and Statistical Package for Social Sciences version 20. Results The study had more females than men (167, 47.71%, vs 183, 52.29%). The observed orbital index of Ghanaians in the study was 81.22 ± 4.22. The mean orbital index was 80.52 ± 4.66 in males and 82.15 ± 3.83 in females with their difference being statistically significant (p value <0.05). This placed both genders in the Microseme category of orbit. There was no significant difference between the orbital index of the two orbital sides (left and right orbits). Conclusion The study found Ghanaians in the category of the Microseme and also indicated a strong sexual dimorphism. The outcome of this study may be useful in forensic medicine for skull classification and also for better surgical approach in neurosurgery as well as cosmetic surgery

Generalisability of fetal ultrasound deep learning models to low-resource imaging settings in five African countries

Most artificial intelligence (AI) research and innovations have concentrated in high-income countries, where imaging data, IT infrastructures and clinical expertise are plentiful. However, slower progress has been made in limited-resource environments where medical imaging is needed. For example, in Sub-Saharan Africa, the rate of perinatal mortality is very high due to limited access to antenatal screening. In these countries, AI models could be implemented to help clinicians acquire fetal ultrasound planes for the diagnosis of fetal abnormalities. So far, deep learning models have been proposed to identify standard fetal planes, but there is no evidence of their ability to generalise in centres with low resources, i.e. with limited access to high-end ultrasound equipment and ultrasound data. This work investigates for the first time different strategies to reduce the domain-shift effect arising from a fetal plane classification model trained on one clinical centre with high-resource settings and transferred to a new centre with low-resource settings. To that end, a classifier trained with 1792 patients from Spain is first evaluated on a new centre in Denmark in optimal conditions with 1008 patients and is later optimised to reach the same performance in five African centres (Egypt, Algeria, Uganda, Ghana and Malawi) with 25 patients each. The results show that a transfer learning approach for domain adaptation can be a solution to integrate small-size African samples with existing large-scale databases in developed countries. In particular, the model can be re-aligned and optimised to boost the performance on African populations by increasing the recall to 0.92±0.04 role= presentation style= box-sizing: inherit; display: inline; line-height: normal; font-size: 18px; word-spacing: normal; word-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; margin: 0px; position: relative; \u3e0.92±0.040.92±0.04 and at the same time maintaining a high precision across centres. This framework shows promise for building new AI models generalisable across clinical centres with limited data acquired in challenging and heterogeneous conditions and calls for further research to develop new solutions for the usability of AI in countries with fewer resources and, consequently, in higher need of clinical support