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

Radiological determination of the cranial index of present-day Ghanaians

Background: The cranial index (CI) of Ghanaians is currently unknown. Objective: The aim of this study was to measure the CI in a population of Ghanaians in order to classify them against pre-determined CI categories. Method: A systematic random sampling method was used to select 300 normal computed tomography (CT) head scans of adult Ghanaians from the largest hospital in Ghana. All patients were deemed to have a normal cranial image configuration based on the radiological report. The biparietal diameter (BPD, width) and the occipitofrontal diameter (OFD, length) were measured on transaxial CT images using a workstation with a calibrated measurement calliper tool. The CI ratio was calculated as the BPD multiplied by 100 and divided by the OFD. Mean, standard deviation (SD) and range was calculated for BPD, OFD and CI. Differences in measurements between demographic groups were compared using an unpaired t-test, with test alpha set at 0.05. Results: Of the population of Ghanaians included in this study, 165 (55%) were male and 135 (45%) were females. The mean CI was 77.30 ± 0.60 in males and 79.0±1.10 in females, placing both genders in the mesocephalic category. However, the difference between males and females was found to be statistically significant (p = 0.02). Conclusion: The study indicated that most Ghanaian adults belong to the mesocephalic category of CI. Females also had a higher CI, which could be used to differentiate gender groups. This information can be useful for forensic medicine, plastic surgeons for clinical and research purpose

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

Future-ai:International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

Endovascular repair of symptomatic abdominal aortic aneurysm: a seminal case in West Africa

Abdominal aortic aneurysm (AAA) is a fatal disease with high perioperative morbidity and mortality. Endovascular AAA repair (EVAR) is associated with remarkable improvement in the morbidity, mortality and length of hospital stay relative to open operative repair. We report a 79-year-old man with epigastric pain, which was diagnosed to be due to AAA on a computerised tomography angiogram (CTA). His only risk factor was hypertension. He had endovascular repair in 2018, the first-ever in Ghana and West Africa. 2021 is the 3rd year of surveillance post- EVAR with no disease progression or complication. This seminal case is a beacon of hope in Ghana’s resource-constrained healthcare system