ReviewLight, science & applications2024
Towards next-generation diagnostic pathology: AI-empowered label-free multiphoton microscopy.
Review in Light, science & applications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed.
- Parallel multilayer photoacoustic microscopy of slide-free histology using a droplet-shaped UV beam.Science advances · 2026Article
- From Label-Free Multiphoton Imaging to Pathological Reports: A Vision-Language Breast Cancer Margin Pathological Diagnosis System.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Review of Machine Learning for Single-Particle Tracking: Methods, Challenges, and Biophysical Insights.Chemical & biomedical imaging · 2026Review
- Quantitative label-free multiphoton imaging of collagen features predicts disease severity and progression in idiopathic pulmonary fibrosis.Respiratory research · 2026Observational
- Artificial intelligence assisted multi-model pathological diagnosis of breast cancer based on multispectral autofluorescence images.NPJ breast cancer · 2026Article
- Optical coherence photoacoustic microscopy for 3D cancer model imaging with AI-assisted organoid analysis.Light, science & applications · 2026Article
- Adapting DeepLabV3+ for biopsy cervical cancer lesion segmentation.Frontiers in digital health · 2026Article
- Second-harmonic generation in NbOINanophotonics (Berlin, Germany) · 2025Article
- Deep learning-enabled multiphoton microscopy predicts colorectal cancer recurrence from routine FFPE specimens.NPJ digital medicine · 2025Article
- On the importance of simultaneous label-free multimodal nonlinear optical imaging for biomedical applications.APL photonics · 2025Article
- Label-free histopathological diagnosis of frozen sections based on multi-excitation and broad-emission autofluorescence imaging.Biomedical optics express · 2025Article
- Investigating machine learning algorithms to classify label-free images of pancreatic neuroendocrine neoplasms.Biophotonics discovery · 2025Article
- Comprehensive linkage between molecular biology and imaging radiomics for thyroid nodules.World journal of radiology · 2025Article
- Multi-photon, label-free photoacoustic and optical imaging of NADH in brain cells.Light, science & applications · 2025Article
- Artificial intelligence for healthcare: restrained development despite impressive applications.Infectious diseases of poverty · 2025Article
- Intravital Imaging of Immune Responses in the Cancer Microenvironment.Cancer medicine · 2025Review
- Dynamic imaging of myelin pathology in physiologically preserved human brain tissue using third harmonic generation microscopy.PloS one · 2025Article
- Molecular Imaging in Early Skin Cancer Detection: Advances, Limitations, and Future Directions.Technology in cancer research & treatmentReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
Diagnostic pathology, historically dependent on visual scrutiny by experts, is essential for disease detection. Advances in digital pathology and developments in computer vision technology have led to the application of artificial intelligence (AI) in this field. Despite these advancements, the variability in pathologists' subjective interpretations of diagnostic criteria can lead to inconsistent outcomes. To meet the need for precision in cancer therapies, there is an increasing demand for accurate pathological diagnoses. Consequently, traditional diagnostic pathology is evolving towards "next-generation diagnostic pathology", prioritizing on the development of a multi-dimensional, intelligent diagnostic approach. Using nonlinear optical effects arising from the interaction of light with biological tissues, multiphoton microscopy (MPM) enables high-resolution label-free imaging of multiple intrinsic components across various human pathological tissues. AI-empowered MPM further improves the accuracy and efficiency of diagnosis, holding promise for providing auxiliary pathology diagnostic methods based on multiphoton diagnostic criteria. In this review, we systematically outline the applications of MPM in pathological diagnosis across various human diseases, and summarize common multiphoton diagnostic features. Moreover, we examine the significant role of AI in enhancing multiphoton pathological diagnosis, including aspects such as image preprocessing, refined differential diagnosis, and the prognostication of outcomes. We also discuss the challenges and perspectives faced by the integration of MPM and AI, encompassing equipment, datasets, analytical models, and integration into the existing clinical pathways. Finally, the review explores the synergy between AI and label-free MPM to forge novel diagnostic frameworks, aiming to accelerate the adoption and implementation of intelligent multiphoton pathology systems in clinical settings.
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.