Human inherited CCR2 deficiency underlies progressive polycystic lung disease.

We describe a human lung disease caused by autosomal recessive, complete deficiency of the monocyte chemokine receptor C-C motif chemokine receptor 2 (CCR2). Nine children from five independent kindreds have pulmonary alveolar proteinosis (PAP), progressive polycystic lung disease, and recurrent infections, including bacillus Calmette Guérin (BCG) disease. The CCR2 variants are homozygous in six patients and compound heterozygous in three, and all are loss-of-expression and loss-of-function. They abolish CCR2-agonist chemokine C-C motif ligand 2 (CCL-2)-stimulated Ca2+ signaling in and migration of monocytic cells. All patients have high blood CCL-2 levels, providing a diagnostic test for screening children with unexplained lung or mycobacterial disease. Blood myeloid and lymphoid subsets and interferon (IFN)-γ- and granulocyte-macrophage colony-stimulating factor (GM-CSF)-mediated immunity are unaffected. CCR2-deficient monocytes and alveolar macrophage-like cells have normal gene expression profiles and functions. By contrast, alveolar macrophage counts are about half. Human complete CCR2 deficiency is a genetic etiology of PAP, polycystic lung disease, and recurrent infections caused by impaired CCL2-dependent monocyte migration to the lungs and infected tissues.

Diversification of human plasmacytoid predendritic cells in response to a single stimulus.

Innate immune cells adjust to microbial and inflammatory stimuli through a process termed environmental plasticity, which links a given individual stimulus to a unique activated state. Here, we report that activation of human plasmacytoid predendritic cells (pDCs) with a single microbial or cytokine stimulus triggers cell diversification into three stable subpopulations (P1-P3). P1-pDCs (PD-L1+CD80-) displayed a plasmacytoid morphology and specialization for type I interferon production. P3-pDCs (PD-L1-CD80+) adopted a dendritic morphology and adaptive immune functions. P2-pDCs (PD-L1+CD80+) displayed both innate and adaptive functions. Each subpopulation expressed a specific coding- and long-noncoding-RNA signature and was stable after secondary stimulation. P1-pDCs were detected in samples from patients with lupus or psoriasis. pDC diversification was independent of cell divisions or preexisting heterogeneity within steady-state pDCs but was controlled by a TNF autocrine and/or paracrine communication loop. Our findings reveal a novel mechanism for diversity and division of labor in innate immune cells.

DOCK11 deficiency in patients with X-linked actinopathy and autoimmunity.

Dedicator of cytokinesis (DOCK) proteins play a central role in actin cytoskeleton regulation. This is highlighted by the DOCK2 and DOCK8 deficiencies leading to actinopathies and immune deficiencies. DOCK8 and DOCK11 activate CDC42, a Rho-guanosine triphosphate hydrolases involved in actin cytoskeleton dynamics, among many cellular functions. The role of DOCK11 in human immune disease has been long suspected but, to the best of our knowledge, has never been described to date. We studied 8 male patients, from 7 unrelated families, with hemizygous DOCK11 missense variants leading to reduced DOCK11 expression. The patients were presenting with early-onset autoimmunity, including cytopenia, systemic lupus erythematosus, skin, and digestive manifestations. Patients' platelets exhibited abnormal ultrastructural morphology and spreading as well as impaired CDC42 activity. In vitro activated T cells and B-lymphoblastoid cell lines from patients exhibited aberrant protrusions and abnormal migration speed in confined channels concomitant with altered actin polymerization during migration. Knock down of DOCK11 recapitulated these abnormal cellular phenotypes in monocytes-derived dendritic cells and primary activated T cells from healthy controls. Lastly, in line with the patients' autoimmune manifestations, we also observed abnormal regulatory T-cell (Treg) phenotype with profoundly reduced FOXP3 and IKZF2 expression. Moreover, we found reduced T-cell proliferation and impaired STAT5B phosphorylation upon interleukin-2 stimulation of the patients' lymphocytes. In conclusion, DOCK11 deficiency is a new X-linked immune-related actinopathy leading to impaired CDC42 activity and STAT5 activation, and is associated with abnormal actin cytoskeleton remodeling as well as Treg phenotype, culminating in immune dysregulation and severe early-onset autoimmunity.

Toll-like Receptor 4 Engagement on Dendritic Cells Restrains Phago-Lysosome Fusion and Promotes Cross-Presentation of Antigens.

The initiation of cytotoxic immune responses by dendritic cells (DCs) requires the presentation of antigenic peptides derived from phagocytosed microbes and infected or dead cells to CD8(+) T cells, a process called cross-presentation. Antigen cross-presentation by non-activated DCs, however, is not sufficient for the effective induction of immune responses. Additionally, DCs need to be activated through innate receptors, like Toll-like receptors (TLRs). During DC maturation, cross-presentation efficiency is first upregulated and then turned off. Here we show that during this transient phase of enhanced cross-presentation, phago-lysosome fusion was blocked by the topological re-organization of lysosomes into perinuclear clusters. LPS-induced lysosomal clustering, inhibition of phago-lysosome fusion and enhanced cross-presentation, all required expression of the GTPase Rab34. We conclude that TLR4 engagement induces a Rab34-dependent re-organization of lysosomal distribution that delays antigen degradation to transiently enhance cross-presentation, thereby optimizing the priming of CD8(+) T cell responses against pathogens.

DNA content and clinicopathological features aid in distinguishing ameloblastic carcinoma from ameloblastoma.

BACKGROUND: Ameloblastoma and ameloblastic carcinoma are epithelial odontogenic tumors that can be morphologically similar. In the present study, we evaluated the DNA content and Ki-67 index in the two tumors. METHODS: The paraffin blocks of the tumors were selected to obtain sections for the immunohistochemical reactions and preparation of the cell suspension for acquisition in a flow cytometer. The Random Forest package of the R software was used to verify the contribution of each variable to classify lesions into ameloblastoma or ameloblastic carcinoma. RESULTS: Thirty-two ameloblastoma and five ameloblastic carcinoma were included in the study. In our sample, we did not find statistically significant differences in Ki-67 labeling rates. A higher fraction of cells in 2c (G1) was correlated with the diagnosis of ameloblastoma, whereas higher rates of 5c-exceeding rate (5cER) were correlated with ameloblastic carcinoma. The Random Forest model highlighted histopathological findings and parameters of DNA ploidy study as important features for distinguishing ameloblastoma from ameloblastic carcinoma. CONCLUSION: Our findings suggest that the parameters of the DNA ploidy study can be ancillary tools in the classification of ameloblastoma and ameloblastic carcinoma.

Feasibility study of ResNet-50 in the distinction of intraoral neural tumors using histopathological images.

BACKGROUND: Neural tumors are difficult to distinguish based solely on cellularity and often require immunohistochemical staining to aid in identifying the cell lineage. This article investigates the potential of a Convolutional Neural Network for the histopathological classification of the three most prevalent benign neural tumor types: neurofibroma, perineurioma, and schwannoma. METHODS: A model was developed, trained, and evaluated for classification using the ResNet-50 architecture, with a database of 30 whole-slide images stained in hematoxylin and eosin (106, 782 patches were generated from and divided among the training, validation, and testing subsets, with strategies to avoid data leakage). RESULTS: The model achieved an accuracy of 70% (64% normalized), and showed satisfactory results for differentiating two of the three classes, reaching approximately 97% and 77% as true positives for neurofibroma and schwannoma classes, respectively, and only 7% for perineurioma class. The AUROC curves for neurofibroma and schwannoma classes was 0.83%, and 0.74% for perineurioma. However, the specificity rate for the perineurioma class was greater (83%) than in the other two classes (neurofibroma with 61%, and schwannoma with 60%). CONCLUSION: This investigation demonstrated significant potential for proficient performance with a limitation regarding the perineurioma class (the limited feature variability observed contributed to a lower performance).

Oral manifestations of amyloidosis and the diagnostic applicability of oral tissue biopsy.

BACKGROUND: Amyloidosis exhibits a variable spectrum of systemic signs and oral manifestations that can be difficult to diagnose. This study aimed to characterize the clinical, demographic, and microscopic features of amyloidosis in the oral cavity. METHODS: This collaborative study involved three Brazilian oral pathology centers and described cases with a confirmed diagnosis of amyloidosis on available oral tissue biopsies. Clinical data were obtained from medical records. H&E, Congo-red, and immunohistochemically stained slides were analyzed. RESULTS: Twenty-six oral biopsies from 23 individuals (65.2% males; mean age: 59.6 years) were included. Oral involvement was the first sign of the disease in 67.0% of cases. Two patients had no clinical manifestation in the oral mucosa, although the histological analysis confirmed amyloid deposition. Amyloid deposits were distributed in perivascular (88.0%), periacinar and periductal (80.0%), perineurial (80.0%), endoneurial (33.3%), perimuscular (88.2%), intramuscular (94.1%), and subepithelial (35.3%) sites as well as around fat cells (100.0%). Mild/moderate inflammation was found in 65.4% of cases and 23.1% had giant cells. CONCLUSIONS: Amyloid deposits were consistently found in oral tissues, exhibiting distinct deposition patterns. Oral biopsy is less invasive than internal organ biopsy and enables the reliable identification of amyloid deposits even in the absence of oral manifestations. These findings corroborate the relevance of oral biopsy for the diagnosis of amyloidosis.

Artificial intelligence and radiomics in the diagnosis of intraosseous lesions of the gnathic bones: A systematic review.

BACKGROUND: The purpose of this systematic review (SR) is to gather evidence on the use of machine learning (ML) models in the diagnosis of intraosseous lesions in gnathic bones and to analyze the reliability, impact, and usefulness of such models. This SR was performed in accordance with the PRISMA 2022 guidelines and was registered in the PROSPERO database (CRD42022379298). METHODS: The acronym PICOS was used to structure the inquiry-focused review question "Is Artificial Intelligence reliable for the diagnosis of intraosseous lesions in gnathic bones?" The literature search was conducted in various electronic databases, including PubMed, Embase, Scopus, Cochrane Library, Web of Science, Lilacs, IEEE Xplore, and Gray Literature (Google Scholar and ProQuest). Risk of bias assessment was performed using PROBAST, and the results were synthesized by considering the task and sampling strategy of the dataset. RESULTS: Twenty-six studies were included (21 146 radiographic images). Ameloblastomas, odontogenic keratocysts, dentigerous cysts, and periapical cysts were the most frequently investigated lesions. According to TRIPOD, most studies were classified as type 2 (randomly divided). The F1 score was presented in only 13 studies, which provided the metrics for 20 trials, with a mean of 0.71 (±0.25). CONCLUSION: There is no conclusive evidence to support the usefulness of ML-based models in the detection, segmentation, and classification of intraosseous lesions in gnathic bones for routine clinical application. The lack of detail about data sampling, the lack of a comprehensive set of metrics for training and validation, and the absence of external testing limit experiments and hinder proper evaluation of model performance.

Histopathological risk assessment in multisite epithelial dysplasia: A meta-analysis.

OBJECTIVE: Histopathological grading of oral epithelial dysplasia (OED) is the current standard for stratifying cancer progression risk but is associated with subjectivity and variability. This problem is not commonly seen regarding the grading of epithelial dysplasia in other sites. This systematic review aims to compare grading systems for oral, anal, penile, and cervical epithelial dysplasia to determine their predictive accuracy for recurrence and malignant transformation (MT) outcomes. METHODS: The review protocol was registered in PROSPERO (CRD42023403035) and was reported according to the PRISMA checklist. A comprehensive search was performed in the main databases and gray literature. The risk of bias in individual studies was analyzed using the Joanna Briggs Institute checklist for each study design. RESULTS: Forty-six studies were deemed eligible and included in this systematic review, of which 45 were included in the quantitative analysis. Meta-analysis revealed that the binary system demonstrated a higher predictive ability for MT/recurrence of OED compared to multilevel systems. Higher predictive accuracy of MT was also observed for binary grading systems in anal intraepithelial neoplasia. CONCLUSIONS: No significant difference was found between the current grading systems of epithelial dysplasia in different body parts. However, binary grading systems have shown better clinical outcomes.

Dimensions of invasiveness: Links between local abundance, geographic range size, and habitat breadth in Europe's alien and native floras.

Understanding drivers of success for alien species can inform on potential future invasions. Recent conceptual advances highlight that species may achieve invasiveness via performance along at least three distinct dimensions: 1) local abundance, 2) geographic range size, and 3) habitat breadth in naturalized distributions. Associations among these dimensions and the factors that determine success in each have yet to be assessed at large geographic scales. Here, we combine data from over one million vegetation plots covering the extent of Europe and its habitat diversity with databases on species' distributions, traits, and historical origins to provide a comprehensive assessment of invasiveness dimensions for the European alien seed plant flora. Invasiveness dimensions are linked in alien distributions, leading to a continuum from overall poor invaders to super invaders-abundant, widespread aliens that invade diverse habitats. This pattern echoes relationships among analogous dimensions measured for native European species. Success along invasiveness dimensions was associated with details of alien species' introduction histories: earlier introduction dates were positively associated with all three dimensions, and consistent with theory-based expectations, species originating from other continents, particularly acquisitive growth strategists, were among the most successful invaders in Europe. Despite general correlations among invasiveness dimensions, we identified habitats and traits associated with atypical patterns of success in only one or two dimensions-for example, the role of disturbed habitats in facilitating widespread specialists. We conclude that considering invasiveness within a multidimensional framework can provide insights into invasion processes while also informing general understanding of the dynamics of species distributions.

Phylogenomics of the Olea europaea complex using 15 whole genomes supports recurrent genetic admixture together with differentiation into seven subspecies.

BACKGROUND: The last taxonomic account of Olea recognises six subspecies within Olea europaea L., including the Mediterranean olive tree (subsp. europaea) and five other subspecies (laperrinei, guanchica, maroccana, cerasiformis, and cuspidata) distributed across the Old World, including Macaronesian islands. The evolutionary history of this monophyletic group (O. europaea complex) has revealed a reticulated scenario involving hybridization and polyploidization events, leading to the presence of a polyploid series associated with the subspecies. However, how the polyploids originated, and how the different subspecies contributed to the domestication of the cultivated olive are questions still debated. Tracing the recent evolution and genetic diversification of the species is key for the management and preservation of its genetic resources. To study the recent history of the O. europaea complex, we compared newly sequenced and available genomes for 27 individuals representing the six subspecies. RESULTS: Our results show discordance between current subspecies distributions and phylogenomic patterns, which support intricate biogeographic patterns. The subspecies guanchica, restricted to the Canary Islands, is closely related to subsp. europaea, and shows a high genetic diversity. The subsp. laperrinei, restricted now to high mountains of the Sahara desert, and the Canarian subsp. guanchica contributed to the formation of the allotetraploid subsp. cerasiformis (Madeira islands) and the allohexaploid subsp. maroccana (western Sahara region). Our phylogenomic data support the recognition of one more taxon (subsp. ferruginea) for the Asian populations, which is clearly segregated from the African subsp. cuspidata. CONCLUSIONS: In sum, the O. europaea complex underwent several processes of hybridization, polyploidy, and geographical isolation resulting in seven independent lineages with certain morphological traits recognised into subspecies.

Actin dynamics regulation by TTC7A/PI4KIIIα limits DNA damage and cell death under confinement.

BACKGROUND: The actin cytoskeleton has a crucial role in the maintenance of the immune homeostasis by controlling various cellular processes, including cell migration. Mutations in TTC7A have been described as the cause of a primary immunodeficiency associated to different degrees of gut involvement and alterations in the actin cytoskeleton dynamics. OBJECTIVES: This study investigates the impact of TTC7A deficiency in immune homeostasis. In particular, the role of the TTC7A/phosphatidylinositol 4 kinase type III α pathway in the control of leukocyte migration and actin dynamics. METHODS: Microfabricated devices were leveraged to study cell migration and actin dynamics of murine and patient-derived leukocytes under confinement at the single-cell level. RESULTS: We show that TTC7A-deficient lymphocytes exhibit an altered cell migration and reduced capacity to deform through narrow gaps. Mechanistically, TTC7A-deficient phenotype resulted from impaired phosphoinositide signaling, leading to the downregulation of the phosphoinositide 3-kinase/AKT/RHOA regulatory axis and imbalanced actin cytoskeleton dynamics. TTC7A-associated phenotype resulted in impaired cell motility, accumulation of DNA damage, and increased cell death in dense 3-dimensional gels in the presence of chemokines. CONCLUSIONS: These results highlight a novel role of TTC7A as a critical regulator of lymphocyte migration. Impairment of this cellular function is likely to contribute to the pathophysiology underlying progressive immunodeficiency in patients.

ChatGPT and dentistry: a step toward the future.

This article aims to explore the integration of ChatGPT, an advanced conversational artificial intelligence model, in the field of dentistry. The review primarily consists of information related to the capabilities and functionalities of ChatGPT and how these abilities can aid dental professionals. This study includes data from research papers, case studies, and relevant literature on language models, as well as papers on dentistry, patient communication, dental education, and clinical decision-making. A systematic approach was used to select relevant studies and literature. The selection criteria focused on papers that specifically discussed the integration of language models, ChatGPT in particular, in dentistry and their applications. The study findings revealed that ChatGPT has significant potential to revolutionize dentistry by offering various applications and benefits. It can enhance patient engagement and understanding through personalized oral health information and guidance. In dental education, ChatGPT can provide interactive learning, case studies, and virtual patient simulations. ChatGPT can also assist researchers in analyzing dental literature, identifying patterns, and generating insights. Moreover, it supports dentists with evidence-based recommendations, treatment options, and diagnostic support. Integrating ChatGPT in dentistry can be highly beneficial, but it is crucial to address ethical considerations, accuracy, and privacy concerns. Responsible implementation and continuous improvement of its functionalities are necessary to ensure that patient care and outcomes are improved.

Development and validation of a multivariable model for prediction of malignant transformation and recurrence of oral epithelial dysplasia.

BACKGROUND: Oral epithelial dysplasia (OED) is the precursor to oral squamous cell carcinoma which is amongst the top ten cancers worldwide. Prognostic significance of conventional histological features in OED is not well established. Many additional histological abnormalities are seen in OED, but are insufficiently investigated, and have not been correlated to clinical outcomes. METHODS: A digital quantitative analysis of epithelial cellularity, nuclear geometry, cytoplasm staining intensity and epithelial architecture/thickness is conducted on 75 OED whole-slide images (252 regions of interest) with feature-specific comparisons between grades and against non-dysplastic/control cases. Multivariable models were developed to evaluate prediction of OED recurrence and malignant transformation. The best performing models were externally validated on unseen cases pooled from four different centres (n = 121), of which 32% progressed to cancer, with an average transformation time of 45 months. RESULTS: Grade-based differences were seen for cytoplasmic eosin, nuclear eccentricity, and circularity in basal epithelial cells of OED (p < 0.05). Nucleus circularity was associated with OED recurrence (p = 0.018) and epithelial perimeter associated with malignant transformation (p = 0.03). The developed model demonstrated superior predictive potential for malignant transformation (AUROC 0.77) and OED recurrence (AUROC 0.74) as compared with conventional WHO grading (AUROC 0.68 and 0.71, respectively). External validation supported the prognostic strength of this model. CONCLUSIONS: This study supports a novel prognostic model which outperforms existing grading systems. Further studies are warranted to evaluate its significance for OED prognostication.

HIF2α is a direct regulator of neutrophil motility.

Orchestrated recruitment of neutrophils to inflamed tissue is essential during the initiation of inflammation. Inflamed areas are usually hypoxic, and adaptation to reduced oxygen pressure is typically mediated by hypoxia pathway proteins. However, it remains unclear how these factors influence the migration of neutrophils to and at the site of inflammation during their transmigration through the blood-endothelial cell barrier, as well as their motility in the interstitial space. Here, we reveal that activation of hypoxia-inducible factor 2 (HIF2α) as a result of a deficiency in HIF prolyl hydroxylase domain protein 2 (PHD2) boosts neutrophil migration specifically through highly confined microenvironments. In vivo, the increased migratory capacity of PHD2-deficient neutrophils resulted in massive tissue accumulation in models of acute local inflammation. Using systematic RNA sequencing analyses and mechanistic approaches, we identified RhoA, a cytoskeleton organizer, as the central downstream factor that mediates HIF2α-dependent neutrophil motility. Thus, we propose that the novel PHD2-HIF2α-RhoA axis is vital to the initial stages of inflammation because it promotes neutrophil movement through highly confined tissue landscapes.

Time-calibrated phylogenies reveal mediterranean and pre-mediterranean origin of the thermophilous vegetation of the Canary Islands.

BACKGROUND AND AIMS: The Canary Islands have strong floristic affinities with the Mediterranean Basin. One of the most characteristic and diverse vegetation belts of the archipelago is the thermophilous woodland (between 200 and 900 m.a.s.l.). This thermophilous plant community consists of many non-endemic species shared with the Mediterranean Floristic Region together with Canarian endemic species. Consequently, phytogeographic studies have historically proposed the hypothesis of an origin of the Canarian thermophilous species following the establishment of the summer-dry mediterranean climate in the Mediterranean Basin around 2.8 million years ago. METHODS: Time-calibrated phylogenies for 39 plant groups including Canarian thermophilous species were primarily analysed to infer colonization times. In particular, we used 26 previously published phylogenies together with 13 new time-calibrated phylogenies (including newly generated plastid and nuclear DNA sequence data) to assess whether the time interval between stem and crown ages of Canarian thermophilous lineages postdates 2.8 Ma. For lineages postdating this time threshold, we additionally conducted ancestral area reconstructions to infer the potential source area for colonization. KEY RESULTS: A total of 43 Canarian thermophilous lineages were identified from 39 plant groups. Both mediterranean (16) and pre-mediterranean (9) plant lineages were found. However, we failed to determine the temporal origin for 18 lineages because a stem-crown time interval overlaps with the 2.8-Ma threshold. The spatial origin of thermophilous lineages was also heterogeneous, including ancestral areas from the Mediterranean Basin (nine) and other regions (six). CONCLUSIONS: Our findings reveal an unexpectedly heterogeneous origin of the Canarian thermophilous species in terms of colonization times and mainland source areas. A substantial proportion of the lineages arrived in the Canaries before the summer-dry climate was established in the Mediterranean Basin. The complex temporal and geographic origin of Canarian thermophilous species challenges the view of the Canary Islands (and Madeira) as a subregion within the Mediterranean Floristic Region.

Spatial distribution of CD34 protein in primordial odontogenic tumour, ameloblastic fibroma and the tooth germ.

BACKGROUND: Primordial odontogenic tumour is a benign mixed neoplasm of recent description, which has histological similarities with other odontogenic tumours such as the ameloblastic fibroma. In this article, we investigate the architecture of the sub-epithelial layer of mesenchymal cells expressing the marker CD34 in primordial odontogenic tumour. OBJECTIVE: Analyse the spatial patterns of CD34 expression in primordial odontogenic tumour and compare them with those in ameloblastic fibroma and the normal tooth germ by means of objective imaging approaches, to better characterise these lesions. METHODS: Two cases of primordial odontogenic tumour, four cases of ameloblastic fibroma and two cases of tooth germ in cap and bell stages were used for morphological, structural and immunohistochemical analyses. RESULTS: CD34 expression was found in vascular endothelium of primordial odontogenic tumour, ameloblastic fibroma and tooth germ. In addition, a characteristic sub-epithelial expression was observed only in primordial odontogenic tumour, corresponding to 84%-86% of the sample boundaries. Moreover, the zone expressing CD34 corresponded with a higher cellularity, which was absent in ameloblastic fibroma and tooth germ. CONCLUSION: Image analysis of the primordial odontogenic tumour architecture revealed characteristics absent in other odontogenic tumours and tooth germs. This study provides additional information to support the idea that this neoplasm is a distinct entity from early stage AF or developing odontoma.

Machine learning for detection and classification of oral potentially malignant disorders: A conceptual review.

Oral potentially malignant disorders represent precursor lesions that may undergo malignant transformation to oral cancer. There are many known risk factors associated with the development of oral potentially malignant disorders, and contribute to the risk of malignant transformation. Although many advances have been reported to understand the biological behavior of oral potentially malignant disorders, their clinical features that indicate the characteristics of malignant transformation are not well established. Early diagnosis of malignancy is the most important factor to improve patients' prognosis. The integration of machine learning into routine diagnosis has recently emerged as an adjunct to aid clinical examination. Increased performances of artificial intelligence AI-assisted medical devices are claimed to exceed the human capability in the clinical detection of early cancer. Therefore, the aim of this narrative review is to introduce artificial intelligence terminology, concepts, and models currently used in oncology to familiarize oral medicine scientists with the language skills, best research practices, and knowledge for developing machine learning models applied to the clinical detection of oral potentially malignant disorders.

Machine learning concepts applied to oral pathology and oral medicine: A convolutional neural networks' approach.

INTRODUCTION: Artificial intelligence models and networks can learn and process dense information in a short time, leading to an efficient, objective, and accurate clinical and histopathological analysis, which can be useful to improve treatment modalities and prognostic outcomes. This paper targets oral pathologists, oral medicinists, and head and neck surgeons to provide them with a theoretical and conceptual foundation of artificial intelligence-based diagnostic approaches, with a special focus on convolutional neural networks, the state-of-the-art in artificial intelligence and deep learning. METHODS: The authors conducted a literature review, and the convolutional neural network's conceptual foundations and functionality were illustrated based on a unique interdisciplinary point of view. CONCLUSION: The development of artificial intelligence-based models and computer vision methods for pattern recognition in clinical and histopathological image analysis of head and neck cancer has the potential to aid diagnosis and prognostic prediction.