Advancing Placental Pathology With Artificial Intelligence: Toward Reproducible and Predictive Diagnostics

Area of Science:

• Pathology
• Medical Artificial Intelligence
• Digital Pathology

Background:

• Placental pathology diagnostics are limited by resource strain, interobserver variability, and a lack of scalable analytic tools.
• Established diagnostic guidelines exist, but clinical demand for placental examination is increasing.
• Current applications of artificial intelligence (AI) and digital pathology in this field are underdeveloped.

Purpose of the Study:

• To review current and future applications of AI in placental pathology.
• To examine challenges and potential solutions for AI implementation in this domain.
• To highlight AI's potential to improve diagnostic reproducibility and predict adverse maternal-fetal outcomes.

Main Methods:

• Synthesis of current literature on AI applications in placental pathology.
• Analysis of AI's role in gross examination, villous maturation, lesion detection, and inflammation assessment.
• Examination of challenges like data scarcity, reproducibility, interpretability, and potential solutions including human-in-the-loop workflows.

Main Results:

• AI models demonstrate strong performance in automated placental gross image analysis, villous maturation assessment, and lesion detection.
• AI effectively quantifies complex features in inflammatory lesion analysis and cell classification.
• Placental morphology is machine-recognizable and quantifiable, indicating AI's potential.

Conclusions:

• AI-driven placental pathology can enhance diagnostic reproducibility and uncover novel disease mechanisms.
• AI offers potential for earlier prediction of adverse maternal-fetal outcomes.
• Clinical translation requires addressing dataset diversity, tissue variability, model interpretability, and strategic investment in digital infrastructure and collaboration.