Background Thyroid hemiagenesis is a rare congenital anomaly in which one lobe of the thyroid gland fails to develop. There is an increased incidence of associated thyroid disorders in patients with thyroid hemiagenesis. Case presentation A 32-year-old Ugandan woman presented with a complaint of painless neck swelling of 3-months duration. The swelling was associated with a globus sensation. There was no history of thyroid – related problems or treatment prior to this presentation. Physical examination demonstrated a mobile right thyroid swelling without an obvious nodular contour. Neck ultrasound showed an absent left lobe of thyroid gland, a right lobe with a solitary nodule scoring two points on the Thyroid Imaging, Reporting and Data System (TI-RADS) and an isthmus in situ. Extensive search for possible ectopic thyroid tissue was negative. She was biochemically euthyroid. The patient was counseled about thyroid hemiagenesis and was put on a regular follow up in the clinic for the TI-RADS 2 nodule. Conclusion Thyroid hemiagenesis is often associated with other thyroid disorders. Its diagnosis should prompt an active search for other associated morphological or functional thyroid abnormalities.

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 $$0.92 \pm 0.04$$ 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 the usability of AI in countries with fewer resources and, consequently, in higher need of clinical support.

Background: Coronavirus disease-2019 (COVID-19) is a potentially life-threatening illness with no established treatment. Cardiovascular risk factors (CRFs) exacerbate COVID-19 morbidity and mortality. Objective: To determine the prevalence of CRF and clinical outcomes of patients hospitalized with COVID-19 in a tertiary hospital in Somalia. Methods: We reviewed the medical records of patients aged 18 years or older with a real-time polymerase chain reaction (RT-PCR)–confirmed COVID-19 hospitalized at the De Martino Hospital in Mogadishu, Somalia, between March and July 2020. Results: We enrolled 230 participants; 159 (69.1%) males, median age was 56 (41–66) years. In-hospital mortality was 19.6% ( n = 45); 77.8% in the intensive care unit (ICU) compared with 22.2%, in the general wards ( p < 0.001). Age ⩾ 40 years [odds ratio (OR): 3.6, 95% confidence interval (CI): 1.2–10.6, p = 0.020], chronic heart disease (OR: 9.3, 95% CI: 2.2–38.9, p = 0.002), and diabetes mellitus (OR: 3.2, 95% CI: 1.6–6.2, p < 0.001) were associated with increased odds of mortality. Forty-three (18.7%) participants required ICU admission. Age ⩾ 40 years (OR: 7.5, 95% CI: 1.7–32.1, p = 0.007), diabetes mellitus (OR: 3.2, 95% CI: 1.6–6.3, p < 0.001), and hypertension (OR: 2.5, 95% CI: 1.2–5.2, p = 0.014) were associated with ICU admission. For every additional CRF, the odds of admission into the ICU increased threefold (OR: 2.7, 95% CI: 1.2–5.2, p < 0.001), while the odds of dying increased twofold (OR: 2.1, 95% CI: 1.3–3.2, p < 0.001). Conclusions: We report a very high prevalence of CRF among patients hospitalized with COVID-19 in Somalia. Mortality rates were unacceptably high, particularly among those with advanced age, underlying chronic heart disease, and diabetes.

Background Accuracy of fetal weight estimation by ultrasound is essential in making decisions on the time and mode of delivery. There are many proposed formulas for fetal weight estimation such as Hadlock 1, Hadlock 2, Hadlock 3, Hadlock 4 and Shepard. What best applies to the Ugandan population is not known since no verification of any of the formulas has been done before. The primary aim of this study was to determine the accuracy of sonographic estimation of fetal weight using five most commonly used formulas, and analyze formula variations for different weight ranges. Methods This was a hospital based prospective cohort study at Mulago National Referral Hospital, Kampala, Uganda. A total of 356 pregnant women who consented and were within 3 days of birth were enrolled. Prenatal ultrasound fetal weight determined by measuring the biparietal diameter, head circumference, abdominal circumference, femoral length, and then was compared with actual birth weight. Results The overall accuracy of Hadlock 1, Hadlock 2, Hadlock 3, Hadlock 4 and Shepard formula were 66.9, 73.3, 77.3, 78.4 and 69.7% respectively. All Hadlocks showed significant mean difference between weight estimates and actual birth weight (p < 0.01) whereas Shepard formula did not [p - 0.2], when no stratification of fetal weights was done. However, all Hadlocks showed a none significant (p-values > 0.05) mean difference between weight estimates and actual birth weight when the actual birth weight was ≥4000.0 g. Shepard weight estimates showed a none significant mean difference when actual birth weight was < 4000 g. Bland-Altman graphs also showed a better agreement of weight estimated by Shepard formula and actual birth weights. Conclusion All the five formulas were accurate at estimating actual birth weights within 10% accuracy. However, this accuracy varied with the fetal birth weight. Shepard was more accurate in estimating actual birth weights < 4000 g whereas all Hadlocks were more accurate when the actual birthweight was ≥4000 g.

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