ArticlePLoS neglected tropical diseases2020
Delimiting cryptic morphological variation among human malaria vector species using convolutional neural networks.
Article in PLoS neglected tropical diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 40 citations in OpenAlex.
- Integration of VectorCam, an AI-Enabled Mosquito Surveillance Tool, Into Uganda's National Malaria Elimination Program: Protocol for a Mixed Methods Hybrid Type III Implementation Study.JMIR public health and surveillance · 2026Article
- Speeding up taxonomy in the digital age: A deep learning approach for identifying cryptic freshwater snails.PLoS computational biology · 2026Article
- Multi-angle, cross-domain fusion strategy enhances automated insect identification and hierarchical categorization: a case study on assassin bugs (Hemiptera: Reduviidae).Cladistics : the international journal of the Willi Hennig Society · 2026Article
- How technologies are driving digital innovation in mosquito surveillance systems: a global scoping review over the past decade.BMJ public health · 2026Article
- Potentials and limitations in the application of Convolutional Neural Networks for mosquito species identification using wing images.PLoS computational biology · 2025Article
- Comprehensive Mosquito Wing Image Repository for Advancing Research on Geometric Morphometric- and AI-Based Identification.Scientific data · 2025Article
- Enhance fashion classification of mosquito vector species via self-supervised vision transformer.Scientific reports · 2024Article
- Towards transforming malaria vector surveillance using VectorBrain: a novel convolutional neural network for mosquito species, sex, and abdomen status identifications.Scientific reports · 2024Article
- Robust mosquito species identification from diverse body and wing images using deep learning.Parasites & vectors · 2024Article
- Automated differentiation of mixed populations of free-flying female mosquitoes under semi-field conditions.Scientific reports · 2024Article
- A convolutional neural network to identify mosquito species (Diptera: Culicidae) of the genus Aedes by wing images.Scientific reports · 2024Article
- Automatic identification of medically important mosquitoes using embedded learning approach-based image-retrieval system.Scientific reports · 2023Article
- Article
- A Swin Transformer-based model for mosquito species identification.Scientific reports · 2022Article
- Scaling up artificial intelligence to curb infectious diseases in Africa.Frontiers in digital health · 2022Article
- A Computer Vision Approach to Identifying Ticks Related to Lyme Disease.IEEE journal of translational engineering in health and medicine · 2022Article
- Article
- Mosquito species identification using convolutional neural networks with a multitiered ensemble model for novel species detection.Scientific reports · 2021Article
- Deep learning approaches for challenging species and gender identification of mosquito vectors.Scientific reports · 2021Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning is a powerful approach for distinguishing classes of images, and there is a growing interest in applying these methods to delimit species, particularly in the identification of mosquito vectors. Visual identification of mosquito species is the foundation of mosquito-borne disease surveillance and management, but can be hindered by cryptic morphological variation in mosquito vector species complexes such as the malaria-transmitting Anopheles gambiae complex. We sought to apply Convolutional Neural Networks (CNNs) to images of mosquitoes as a proof-of-concept to determine the feasibility of automatic classification of mosquito sex, genus, species, and strains using whole-body, 2D images of mosquitoes. We introduce a library of 1, 709 images of adult mosquitoes collected from 16 colonies of mosquito vector species and strains originating from five geographic regions, with 4 cryptic species not readily distinguishable morphologically even by trained medical entomologists. We present a methodology for image processing, data augmentation, and training and validation of a CNN. Our best CNN configuration achieved high prediction accuracies of 96.96% for species identification and 98.48% for sex. Our results demonstrate that CNNs can delimit species with cryptic morphological variation, 2 strains of a single species, and specimens from a single colony stored using two different methods. We present visualizations of the CNN feature space and predictions for interpretation of our results, and we further discuss applications of our findings for future applications in malaria mosquito surveillance.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.