Parasite Recognition Research Articles

The myxozoans Myxobolus cerebralis and Tetracapsuloides bryosalmonae modulate rainbow trout immune responses: quantitative shotgun proteomics at the portals of entry after single and co-infections.

Little is known about the proteomic changes at the portals of entry in rainbow trout after infection with the myxozoan parasites, Myxobolus cerebralis, and Tetracapsuloides bryosalmonae. Whirling disease (WD) is a severe disease of salmonids, caused by the myxosporean M. cerebralis, while, proliferative kidney disease (PKD) is caused by T. bryosalmonae, which instead belongs to the class Malacosporea. Climate change is providing more suitable conditions for myxozoan parasites lifecycle, posing a high risk to salmonid aquaculture and contributing to the decline of wild trout populations in North America and Europe. Therefore, the aim of this study was to provide the first proteomic profiles of the host in the search for evasion strategies during single and coinfection with M. cerebralis and T. bryosalmonae. One group of fish was initially infected with M. cerebralis and another group with T. bryosalmonae. After 30 days, half of the fish in each group were co-infected with the other parasite. Using a quantitative proteomic approach, we investigated proteomic changes in the caudal fins and gills of rainbow trout before and after co-infection. In the caudal fins, 16 proteins were differentially regulated post exposure to M. cerebralis, whereas 27 proteins were differentially modulated in the gills of the infected rainbow trout post exposure to T. bryosalmonae. After co-infection, 4 proteins involved in parasite recognition and the regulation of host immune responses were differentially modulated between the groups in the caudal fin. In the gills, 11 proteins involved in parasite recognition and host immunity, including 4 myxozoan proteins predicted to be virulence factors, were differentially modulated. The results of this study increase our knowledge on rainbow trout co-infections by myxozoan parasites and rainbow trout immune responses against myxozoans at the portals of entry, supporting a better understanding of these host-parasite interactions.

AI DRIVEN BLOOD CELL ANALYSIS FOR MALARIA AND BLOOD CANCER SCREENING

Malaria and blood Cancer is one of the deadliest diseases cross the globe. This is caused by the bite of female Anopheles mosquito that transmits the Plasmodium parasites. Some current malaria detection techniques include manual microscopic examination and RDT. These approaches are vulnerable to human mistakes. Early detection of malaria can help in reducing the death rates across the globe.Deep Learning can emerge as a highly beneficial solution in the diagnosis of disease. This model gives a faster and cheaper method for detecting plasmodium parasites. It Was Designed to Identify Malaria Parasites and Cancerous Cell Presence in Blood Using Images of Blood Samples which got Tested with Giemsa-stain. Convolutional neural networks (CNNs) are then trained on these extracted features to accurately classify blood cell images into disease positive or disease-negative categories. The CNN detects malaria parasites in microscopic images by classifying them into parasitized and healthy cells, enabling the detection of malaria parasites. The proposed model consists of three convolutional layers and fully connected layers each. The neural network presented is a cascade of several convolutional layers having multiple filters present in layers, which yields the exceptionally good accuracy as per the available resources. The experiment results demonstrate a significant improvement in malaria parasite and blood cancer recognition. Compared to traditional manual microscopy, the proposed system is more accurate and faster. Finally, this study demonstrates the need to provide robust and efficient solutions by leveraging state-of-the-art technologies to combat malaria and blood cancer. Keywords : Deep Learning, CNN, Image analysis , InceptionresnetV2

The Anopheles leucine-rich repeat protein APL1C is a pathogen binding factor recognizing Plasmodium ookinetes and sporozoites.

Leucine-rich repeat (LRR) proteins are commonly involved in innate immunity of animals and plants, including for pattern recognition of pathogen-derived elicitors. The Anopheles secreted LRR proteins APL1C and LRIM1 are required for malaria ookinete killing in conjunction with the complement-like TEP1 protein. However, the mechanism of parasite immune recognition by the mosquito remains unclear, although it is known that TEP1 lacks inherent binding specificity. Here, we find that APL1C and LRIM1 bind specifically to Plasmodium berghei ookinetes, even after depletion of TEP1 transcript and protein, consistent with a role for the LRR proteins in pathogen recognition. Moreover, APL1C does not bind to ookinetes of the human malaria parasite Plasmodium falciparum, and is not required for killing of this parasite, which correlates LRR binding specificity and immune protection. Most of the live P. berghei ookinetes that migrated into the extracellular space exposed to mosquito hemolymph, and almost all dead ookinetes, are bound by APL1C, thus associating LRR protein binding with parasite killing. We also find that APL1C binds to the surface of P. berghei sporozoites released from oocysts into the mosquito hemocoel and forms a potent barrier limiting salivary gland invasion and mosquito infectivity. Pathogen binding by APL1C provides the first functional explanation for the long-known requirement of APL1C for P. berghei ookinete killing in the mosquito midgut. We propose that secreted mosquito LRR proteins are required for pathogen discrimination and orientation of immune effector activity, potentially as functional counterparts of the immunoglobulin-based receptors used by vertebrates for antigen recognition.

Enhancing medical image analysis with unsupervised domain adaptation approach across microscopes and magnifications

In the domain of medical image analysis, deep learning models are heralding a revolution, especially in detecting complex and nuanced features characteristic of diseases like tumors and cancers. However, the robustness and adaptability of these models across varied imaging conditions and magnifications remain a formidable challenge. This paper introduces the Fourier Adaptive Recognition System (FARS), a pioneering model primarily engineered to address adaptability in malarial parasite recognition. Yet, the foundational principles guiding FARS lend themselves seamlessly to broader applications, including tumor and cancer diagnostics. FARS capitalizes on the untapped potential of transitioning from bounding box labels to richer semantic segmentation labels, enabling a more refined examination of microscopy slides. With the integration of adversarial training and the Color Domain Aware Fourier Domain Adaptation (F2DA), the model ensures consistent feature extraction across diverse microscopy configurations. The further inclusion of category-dependent context attention amplifies FARS’s cross-domain versatility. Evidenced by a substantial elevation in cross-magnification performance from 31.3% mAP to 55.19% mAP and a 15.68% boost in cross-domain adaptability, FARS positions itself as a significant advancement in malarial parasite recognition. Furthermore, the core methodologies of FARS can serve as a blueprint for enhancing precision in other realms of medical image analysis, especially in the complex terrains of tumor and cancer imaging. The code is available at; https://github.com/Mr-TalhaIlyas/FARS.

Multiclass malaria parasite recognition based on transformer models and a generative adversarial network

Malaria is an extremely infectious disease and a main cause of death worldwide. Microscopic examination of thin slide serves as a common method for the diagnosis of malaria. Meanwhile, the transformer models have gained increasing popularity in many regions, such as computer vision and natural language processing. Transformers also offer lots of advantages in classification task, such as Fine-grained Feature Extraction, Attention Mechanism etc. In this article, we propose to assist the medical professionals by developing an effective framework based on transformer models and a generative adversarial network for multi-class plasmodium classification and malaria diagnosis. The Generative Adversarial Network is employed to generate extended training samples from multiclass cell images, with the aim of enhancing the robustness of the resulting model. We aim to optimize plasmodium classification to achieve an exact balance of high accuracy and low resource consumption. A comprehensive comparison of the transformer models to the state-of-the-art methods proves their efficiency in the classification of malaria parasite through thin blood smear microscopic images. Based on our findings, the Swin Transformer model and MobileVit outperform the baseline architectures in terms of precision, recall, F1-score, specificity, and FPR on test set (the data was divided into train: validation: test splits). It is evident that the Swin Transformer achieves superior detection performance (up to 99.8% accuracy), while MobileViT demonstrates lower memory usage and shorter inference times. High accuracy empowers healthcare professionals to conduct precise diagnoses, while low memory usage and short inference times enable the deployment of predictive models on edge devices with limited computational and memory resources.

Parasitic egg recognition using convolution and attention network

Intestinal parasitic infections (IPIs) caused by protozoan and helminth parasites are among the most common infections in humans in low-and-middle-income countries. IPIs affect not only the health status of a country, but also the economic sector. Over the last decade, pattern recognition and image processing techniques have been developed to automatically identify parasitic eggs in microscopic images. Existing identification techniques are still suffering from diagnosis errors and low sensitivity. Therefore, more accurate and faster solution is still required to recognize parasitic eggs and classify them into several categories. A novel Chula-ParasiteEgg dataset including 11,000 microscopic images proposed in ICIP2022 was utilized to train various methods such as convolutional neural network (CNN) based models and convolution and attention (CoAtNet) based models. The experiments conducted show high recognition performance of the proposed CoAtNet that was tuned with microscopic images of parasitic eggs. The CoAtNet produced an average accuracy of 93%, and an average F1 score of 93%. The finding opens door to integrate the proposed solution in automated parasitological diagnosis.

Intelligent diagnostic model for malaria parasite detection and classification using imperative inception-based capsule neural networks

Malaria is an acute fever sickness caused by the Plasmodium parasite and spread by infected Anopheles female mosquitoes. It causes catastrophic illness if left untreated for an extended period, and delaying exact treatment might result in the development of further complications. The most prevalent method now available for detecting malaria is the microscope. Under a microscope, blood smears are typically examined for malaria diagnosis. Despite its advantages, this method is time-consuming, subjective, and requires highly skilled personnel. Therefore, an automated malaria diagnosis system is imperative for ensuring accurate and efficient treatment. This research develops an innovative approach utilizing an urgent, inception-based capsule network to distinguish parasitized and uninfected cells from microscopic images. This diagnostic model incorporates neural networks based on Inception and Imperative Capsule networks. The inception block extracts rich characteristics from images of malaria cells using a pre-trained model, such as Inception V3, which facilitates efficient representation learning. Subsequently, the dynamic imperative capsule neural network detects malaria parasites in microscopic images by classifying them into parasitized and healthy cells, enabling the detection of malaria parasites. The experiment results demonstrate a significant improvement in malaria parasite recognition. Compared to traditional manual microscopy, the proposed system is more accurate and faster. Finally, this study demonstrates the need to provide robust and efficient diagnostic solutions by leveraging state-of-the-art technologies to combat malaria.

Non-specific cytotoxic cell receptor protein-1 (NCCRP-1) is involved in anti-parasite innate CD8+ T cell-mediated cytotoxicity in ginbuna crucian carp.

CD8+ cytotoxic T cells (CTLs) are a main cellular component of adaptive immunity. Our previous research has shown that CD8+ cells demonstrate spontaneous cytotoxic activity against the parasite Ichthyophthirius multifiliis in ginbuna crucian carp, suggesting that CD8+ cells play an important role in innate immunity. Herein, we investigated the molecules and cellular signal pathways involved in the cytotoxic response of ginbuna crucian carp. We considered non-specific cytotoxic receptor protein-1 (NCCRP-1) as candidate molecule for parasite recognition. We detected NCCRP-1 protein in CD8+ cells and the thymus as well as in other cells and tissues. CD8+ cells expressed mRNA for NCCRP-1, Jak2, and T cell-related molecules. In addition, treatment with a peptide containing the presumed antigen recognition site of ginbuna NCCRP-1 significantly inhibited the cytotoxic activity of CD8+ cells against the parasites. The cytotoxic activity of CD8+ cells was significantly inhibited by treatment with the JAK1/2 inhibitor baricitinib. These results suggest that teleost CTLs recognize I. multifiliis through NCCRP-1 and are activated by JAK/STAT signaling.

Revealing the roles of egg darkness and nest similarity for a cryptic parasite egg versus host's cognition: an alternate coevolutionary trajectory.

As a text-book example of coevolution, the escalating interactions between egg mimicry by parasitic cuckoos and egg recognition by their hosts constitute a key battlefield for parasitism and anti-parasitism strategies. However, some parasite-host systems have deviated from this coevolutionary trajectory because some cuckoos do not lay mimetic eggs, while the hosts do not recognize them, even under the high costs of parasitism. The cryptic egg hypothesis was proposed to explain this puzzle, but the evidence to date is mixed and the relationship between the two components of egg crypticity, egg darkness (dim egg coloration) and nest similarity (similarity to host nest appearance), remains unknown. Here, we developed a 'field psychophysics' experimental design to dissect these components while controlling for undesired confounding factors. Our results clearly show that both egg darkness and nest similarity of cryptic eggs affect recognition by hosts, and egg darkness plays a more influential role than nest similarity. This study provides unambiguous evidence to resolve the puzzle of absent mimicry and recognition in cuckoo-host systems and explains why some cuckoo eggs were more likely to evolve dim coloration rather than similarity to host eggs or host nests.

House sparrows do not exhibit a preference for the scent of potential partners with different MHC-I diversity and genetic distances.

MHC genes play a fundamental role in immune recognition of pathogens and parasites. Therefore, females may increase offspring heterozygosity and genetic diversity by selecting males with genetically compatible or heterozygous MHC. In birds, several studies suggest that MHC genes play a role in mate choice, and recent evidence suggests that olfaction may play a role in the MHC-II discrimination. However, whether olfaction is involved in MHC-I discrimination in birds remains unknown. Previous studies indicate that house sparrow females with low allelic diversity prefer males with higher diversity in MHC-I alleles. Here, we directly explored whether female and male house sparrows (Passer domesticus) could estimate by scent MHC-I diversity and/or dissimilarity of potential partners. Our results show that neither females nor males exhibit a preference related to MHC-I diversity or dissimilarity of potential partners, suggesting that MHC-I is not detected through olfaction. Further studies are needed to understand the mechanisms responsible for mate discrimination based on MHC-I in birds.

Semi-supervised graph learning framework for apicomplexan parasite classification

Apicomplexan parasites cause diseases including malaria, toxoplasmosis, and babesiosis, affecting large parts of the world and hampering economic development to a considerable severity. Early recognition of pathogenic parasites can effectively constrain the spread of these infectious diseases. Recently, deep-learning-based computer-aided parasite recognition systems provide many practical tools for automated apicomplexan parasite recognition. However, existing research works are limited by the complicated and expensive manual annotating workload in supervised deep learning methods, which require many experts’ manually annotated samples. Moreover, they also ignore the significance of correlations among different parasite cells, which retain helpful information for identification. Therefore, this paper is the first to employ a semi-supervised learning strategy to reduce the manual labeling workload and introduce a hybrid graph learning algorithm into parasite recognition. Our approach has a superior parasite recognition capability with semi-supervised techniques and the graph-based model. Specifically, this research proposes a Semi-Supervised Graph Learning (SSGL) framework, composing CNN (Convolutional Neural Networks) feature embedding, learnable graph building, and graph feature learning components. To verify the performance of the SSGL model, the paper applies multiple evaluation indicators to appraise our model, and the approach can achieve high accuracy of 91.75%, AUC of 91.83%, the sensitivity of 91.75% and specificity of 97.25% with only a small amount of labeled data (20%). In conclusion, SSGL model provides high parasite recognition capability with minimal labels, greatly reduces the workload of relevant experts, and promotes the popularization of computer-aided parasite diagnosis tools.

Advances Towards Automatic Detection and Classification of Parasites Microscopic Images Using Deep Convolutional Neural Network: Methods, Models and Research Directions.

In the developing world, parasites are responsible for causing several serious health problems, with relatively high infections in human beings. The traditional manual light microscopy process of parasite recognition remains the golden standard approach for the diagnosis of parasitic species, but this approach is time-consuming, highly tedious, and also difficult to maintain consistency but essential in parasitological classification for carrying out several experimental observations. Therefore, it is meaningful to apply deep learning to address these challenges. Convolution Neural Network and digital slide scanning show promising results that can revolutionize the clinical parasitology laboratory by automating the process of classification and detection of parasites. Image analysis using deep learning methods have the potential to achieve high efficiency and accuracy. For this review, we have conducted a thorough investigation in the field of image detection and classification of various parasites based on deep learning. Online databases and digital libraries such as ACM, IEEE, ScienceDirect, Springer, and Wiley Online Library were searched to identify sufficient related paper collections. After screening of 200 research papers, 70 of them met our filtering criteria, which became a part of this study. This paper presents a comprehensive review of existing parasite classification and detection methods and models in chronological order, from traditional machine learning based techniques to deep learning based techniques. In this review, we also demonstrate the summary of machine learning and deep learning methods along with dataset details, evaluation metrics, methods limitations, and future scope over the one decade. The majority of the technical publications from 2012 to the present have been examined and summarized. In addition, we have discussed the future directions and challenges of parasites classification and detection to help researchers in understanding the existing research gaps. Further, this review provides support to researchers who require an effective and comprehensive understanding of deep learning development techniques, research, and future trends in the field of parasites detection and classification.

Barnacles Mating Optimizer with Deep Transfer Learning Enabled Biomedical Malaria Parasite Detection and Classification.

Biomedical engineering involves ideologies and problem-solving methods of engineering to biology and medicine. Malaria is a life-threatening illness, which has gained significant attention among researchers. Since the manual diagnosis of malaria in a clinical setting is tedious, automated tools based on computational intelligence (CI) tools have gained considerable interest. Though earlier studies were focused on the handcrafted features, the diagnostic accuracy can be boosted through deep learning (DL) methods. This study introduces a new Barnacles Mating Optimizer with Deep Transfer Learning Enabled Biomedical Malaria Parasite Detection and Classification (BMODTL-BMPC) model. The presented BMODTL-BMPC model involves the design of intelligent models for the recognition and classification of malaria parasites. Initially, the Gaussian filtering (GF) approach is employed to eradicate noise in blood smear images. Then, Graph cuts (GC) segmentation technique is applied to determine the affected regions in the blood smear images. Moreover, the barnacles mating optimizer (BMO) algorithm with the NasNetLarge model is employed for the feature extraction process. Furthermore, the extreme learning machine (ELM) classification model is employed for the identification and classification of malaria parasites. To assure the enhanced outcomes of the BMODTL-BMPC technique, a wide-ranging experimentation analysis is performed using a benchmark dataset. The experimental results show that the BMODTL-BMPC technique outperforms other recent approaches.

Defensive adaptations to cuckoo parasitism in the black-browed reed warbler (Acrocephalus bistrigiceps): recognition and mechanism.

Hosts of avian brood parasites suffer a high cost of reproductive loss due to parasitism, driving them to evolve a variety of anti-parasitic defenses. These defenses comprise a series of components, including the recognition of brood parasites and the eggs laid by the parasites, cues used for recognition, and the mechanisms on which these behaviors are based. In this study, we conducted egg recognition and nest intruder experiments to examine these components of anti-parasitic behavior in the black-browed reed warbler (Acrocephalus bistrigiceps), a rare host of the common cuckoo (Cuculus canorus). We found that the host possessed strong recognition capacity, rejecting 100% of parasitic eggs, and used a template-based mechanism for egg recognition. The host birds also rejected 80% of their own eggs on which artificial markings were added to the blunt pole; however, they accepted all eggs with the same manipulation on the sharp pole, implying that the blunt pole was an important recognition cue. Furthermore, the host exhibited stronger aggression to cuckoos than to harmless controls; a behavior specific to the incubation stage rather than the nestling stage. Therefore, the host was able to distinguish the cuckoo from other nest intruders as being a brood parasite. These results together help explain the near absence of cuckoo parasitism in black-browed reed warblers and provide new information concerning anti-parasitic defenses in this host species.

Neighbor Correlated Graph Convolutional Network for multi-stage malaria parasite recognition

Neighbor Correlated Graph Convolutional Network for multi-stage malaria parasite recognition

Early‐life environmental conditions influence parasitism at adulthood and life‐history of a cuckoo host

Environmental conditions experienced by individuals early in life can extend into adult phenotypes with potential fitness consequences. Such environmental effects can be relevant for host species affected by parasitism, because poor early life conditions may lower parasite recognition or anti‐parasite defenses and increase the risk of future parasitism. Here we provide a first test of this possibility by using data from a 16‐year study of individually marked female magpiePica picahosts for which we know natal and adult environments, occurrence of great spotted cuckooClamator glandariusparasitism, egg discrimination ability and life history in detail. Females born in warmer years were more likely to be parasitized at adulthood and produced fewer offspring throughout their life. Egg discrimination behavior and lifespan of magpies were not influenced by the quality of natal environments. Our results provide support for the hypothesis that annual environmental variation promotes cohort effects in magpie hosts that may have an impact on cuckoo–host co‐evolutionary dynamics.

Genetic polymorphism of microsatellite loci in MHC class II exon 2 gene and its association with endoparasitic infestation, predominantly Haemonchus contortus in Salem black goat

MHC gene is highly polymorphic and plays central molecule in immune system in recognition of pathogens and parasites. The present study examined the variability of MHC class II DRB exon 2 gene using microsatellite analysis, and its association with Haemonchus contortus infestation in Salem Black goat population. Animals were naturally exposed to mixed infestation of endoparasites, predominantly Haemonchus contortus. Pooled fecal coproculture and larval identification showed predominant presence of haemonchus (L3) larva. Fecal egg count (FEC) and packed cell volume (PCV) were used as indicator traits. All the three studied loci, OarCP73, DYA, and ODRB1.2, were polymorphic having three, four, and four alleles and four, eight, and nine genotypes, respectively. The loci showed low to moderate values of polymorphic information content. The mean fecal egg count estimates were 477.12 ± 34.14 eggs per gram of feces and the mean PCV values were within the normal range; however, they showed negative correlation with FEC values. All the three microsatellite loci and the effect of genotypes on indicator traits were found to be statistically significant (p ≤ 0.05). The present study will be helpful in selection and breeding of goats for Haemonchosis resistance.

Differential investment in visual and olfactory brain regions is linked to the sensory needs of a wasp social parasite and its host.

Obligate insect social parasites evolve traits to effectively locate and then exploit their hosts, whereas hosts have complex social behavioral repertoires, which include sensory recognition to reject potential conspecific intruders and heterospecific parasites. While social parasites and host behaviors have been studied extensively, less is known about how their sensory systems function to meet their specific selective pressures. Here, we compare investment in visual and olfactory brain regions in the paper wasp Polistes dominula, and its obligate social parasite P. sulcifer, to explore the links among sensory systems,brain and behavior. Our results show significant relative volumetric differences between these two closely related species, consistent with their very different life histories. Social parasites show proportionally larger optic lobes and central complex to likely navigate long-distance migrations and unfamiliar landscapes to locate the specific species of hosts they usurp. Contrastingly, hosts have larger antennal lobes and calyces of the mushroom bodies compared with social parasites, as predicted by their sensory means to maintain social cohesion via olfactory signals, allocate colony tasks, forage, and recognize conspecific and heterospecific intruders. Our work suggests how this tradeoff between visual and olfactory brain regions may facilitate different sensory adaptations needed to perform social and foraging tasks by the host, including recognition of parasites, or to fly long distances and successful host localizing by the social parasite.

The Paradox of a Phagosomal Lifestyle: How Innate Host Cell-Leishmania amazonensis Interactions Lead to a Progressive Chronic Disease.

Intracellular phagosomal pathogens represent a formidable challenge for innate immune cells, as, paradoxically, these phagocytic cells can act as both host cells that support pathogen replication and, when properly activated, are the critical cells that mediate pathogen elimination. Infection by parasites of the Leishmania genus provides an excellent model organism to investigate this complex host-pathogen interaction. In this review we focus on the dynamics of Leishmania amazonensis infection and the host innate immune response, including the impact of the adaptive immune response on phagocytic host cell recruitment and activation. L. amazonensis infection represents an important public health problem in South America where, distinct from other Leishmania parasites, it has been associated with all three clinical forms of leishmaniasis in humans: cutaneous, muco-cutaneous and visceral. Experimental observations demonstrate that most experimental mouse strains are susceptible to L. amazonensis infection, including the C57BL/6 mouse, which is resistant to other species such as Leishmania major, Leishmania braziliensis and Leishmania infantum. In general, the CD4+ T helper (Th)1/Th2 paradigm does not sufficiently explain the progressive chronic disease established by L. amazonensis, as strong cell-mediated Th1 immunity, or a lack of Th2 immunity, does not provide protection as would be predicted. Recent findings in which the balance between Th1/Th2 immunity was found to influence permissive host cell availability via recruitment of inflammatory monocytes has also added to the complexity of the Th1/Th2 paradigm. In this review we discuss the roles played by innate cells starting from parasite recognition through to priming of the adaptive immune response. We highlight the relative importance of neutrophils, monocytes, dendritic cells and resident macrophages for the establishment and progressive nature of disease following L. amazonensis infection.

Host and brood parasite coevolutionary interactions covary with comparative patterns of the avian visual system

In coevolutionary arms-races, reciprocal ecological interactions and their fitness impacts shape the course of phenotypic evolution. The classic example of avian host–brood parasite interactions selects for host recognition and rejection of increasingly mimetic foreign eggs. An essential component of perceptual mimicry is that parasitic eggs escape detection by host sensory systems, yet there is no direct evidence that the avian visual system covaries with parasitic egg recognition or mimicry. Here, we used eye size measurements collected from preserved museum specimens as a metric of the avian visual system for species involved in host–brood parasite interactions. We discovered that (i) hosts had smaller eyes compared with non-hosts, (ii) parasites had larger eyes compared with hosts before but not after phylogenetic corrections, perhaps owing to the limited number of independent evolutionary origins of obligate brood parasitism, (iii) egg rejection in hosts with non-mimetic parasitic eggs positively correlated with eye size, and (iv) eye size was positively associated with increased avian-perceived host–parasite eggshell similarity. These results imply that both host-use by parasites and anti-parasitic responses by hosts covary with a metric of the visual system across relevant bird species, providing comparative evidence for coevolutionary patterns of host and brood parasite sensory systems.