Automated retinal lesion detection via image saliency analysis

Retinal imaging technologies in cerebral malaria: a systematic review

BACKGROUND

Cerebral malaria (CM) continues to present a major health challenge, particularly in sub-Saharan Africa. CM is associated with a characteristic malarial retinopathy (MR) with diagnostic and prognostic significance. Advances in retinal imaging have allowed researchers to better characterize the changes seen in MR and to make inferences about the pathophysiology of the disease. The study aimed to explore the role of retinal imaging in diagnosis and prognostication in CM; establish insights into pathophysiology of CM from retinal imaging; establish future research directions.

METHODS

The literature was systematically reviewed using the African Index Medicus, MEDLINE, Scopus and Web of Science databases. A total of 35 full texts were included in the final analysis. The descriptive nature of the included studies and heterogeneity precluded meta-analysis.

RESULTS

Available research clearly shows retinal imaging is useful both as a clinical tool for the assessment of CM and as a scientific instrument to aid the understanding of the condition. Modalities which can be performed at the bedside, such as fundus photography and optical coherence tomography, are best positioned to take advantage of artificial intelligence-assisted image analysis, unlocking the clinical potential of retinal imaging for real-time diagnosis in low-resource environments where extensively trained clinicians may be few in number, and for guiding adjunctive therapies as they develop.

CONCLUSIONS

Further research into retinal imaging technologies in CM is justified. In particular, co-ordinated interdisciplinary work shows promise in unpicking the pathophysiology of a complex disease.

Automated malarial retinopathy detection using transfer learning and multi-camera retinal images

Cerebral malaria (CM) is a fatal syndrome found commonly in children less than 5 years old in Sub-saharan Africa and Asia. The retinal signs associated with CM are known as malarial retinopathy (MR), and they include highly specific retinal lesions such as whitening and hemorrhages. Detecting these lesions allows the detection of CM with high specificity. Up to 23% of CM, patients are over-diagnosed due to the presence of clinical symptoms also related to pneumonia, meningitis, or others. Therefore, patients go untreated for these pathologies, resulting in death or neurological disability. It is essential to have a low-cost and high-specificity diagnostic technique for CM detection, for which We developed a method based on transfer learning (TL). Models pre-trained with TL select the good quality retinal images, which are fed into another TL model to detect CM. This approach shows a 96% specificity with low-cost retinal cameras.

[Regional classification-guided wavelet Y-Net network for hard exudate segmentation in fundus images]

OBJECTIVE

We propose an hard exudate(EX)segmentation algorithm based on regional classification-guided wavelet Y-Net network to eliminate the influence of optic disc on EX segmentation process.

METHODS

The wavelet Y-Net network was an end-to-end fundus image EX segmentation network, which combined the regional detection of optic disc and hard exudates segmentation by regional classification-guided EX segmentation to effectively reduce the interference of optic disc in EX segmentation.To avoid failure of small EX region segmentation caused by information loss due to down-sampling operation, discrete wavelet transform (DWT) and inverse discrete wavelet transform (IDWT) were introduced to replace the traditional pooling down-sampling and up-sampling operations.Meanwhile, the inception module based on residual connection was used to obtain the multi-scale features.The proposed algorithm was trained and tested on the IDRiD and e-ophtha EX datasets and evaluated at the pixel level.

RESULTS

For IDRiD and e-ophtha EX datasets, the proposed algorithm achieved accuracy rates of 0.9858 and 0.9938 with AUC values of 0.9880 and 0.9986, respectively.

CONCLUSION

The proposed method can effectively avoid the influence of the optic disc, retain the image details, and improve the effect of EX segmentation.