Advances and challenges in automated malaria diagnosis using digital microscopy imaging with artificial intelligence tools: A review
importance 8/10
ai assisted
Digital Imaging for Blood Diseases infectious disease diagnostics stage: research
Problem
Malaria diagnosis by manual microscopy is slow and requires a well-trained microscopist, limiting diagnosis in resource-poor settings.
Why it matters
Malaria caused 241 million cases in 2020, mostly in settings needing fast, low-cost diagnosis.
What the AI did
Reviews CNN-based image-analysis systems that emulate expert microscopy to detect malaria parasites, including smartphone-based capture and identification.
Results
not stated in source
Impact
Positions AI microscopy as a fast, low-cost diagnostic alternative, though challenges remain.
Record
| Venue | Frontiers in Microbiology |
|---|---|
| Published | 2022 |
| Work type | article |
| Authors | Carles Rubio Maturana, Allisson Dantas de Oliveira, Sergi Nadal, Besim Bilalli, Francesc Zarzuela, Mateu Espasa, Elena Sulleiro, Mercedes Bosch et al. |
| Citations | 87 (OpenAlex, at import time) |
| References | 144 |
| Model or method | Convolutional Neural Networks |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3389/fmicb.2022.1006659 |
| Open access | gold |
| Keywords | malaria, deep learning, CNN, digital microscopy, smartphone diagnosis |
Importance rating
8
major — millions of lives, an industry or a field
Malaria diagnosis in resource-poor settings, with 241 million cases reported in 2020; AI microscopy offers fast, low-cost alternative to manual expert microscopy.