Background

Cervical cancer remains a major public health challenge in India, with approximately 120,000–125,000 new cases and over 75,000 deaths annually, making it the second most common cancer among Indian women. Although cervical cancer is largely preventable through early detection and timely treatment, less than 5–7% of eligible women in India undergo regular screening. Key barriers include limited access to screening services, high costs of existing technologies, and dependence on centralized tertiary-care laboratories. These challenges are particularly acute in tier-2 and tier-3 cities, where cervical cancer screening is predominantly delivered through small laboratories and nursing homes with limited infrastructure and shortage of trained pathology manpower.

Objectives

The objective of this work was to develop and evaluate an affordable, automated, and scalable cervical cancer screening platform suitable for decentralized settings in India, aimed at reducing pathologist workload, improving screening throughput, and enabling wider access to quality cytology services in tier-2 and tier-3 healthcare facilities.

Methods

We developed two complementary automated technologies tailored for decentralized screening: Autostain-PAP, an automated cytology smear preparation system capable of processing four samples per hour, and Autoscope, an automated whole-slide imaging system capable of scanning six cytology slides per hour. The technical performance of the Autostain-PAP smear preparation process was benchmarked against the BD SurePath™ liquid-based cytology system to assess slide quality and preparation consistency. In addition, Autoscope is integrated with an AI-assisted cytology analysis model that classifies cervical epithelial cells into normal, low-grade squamous intraepithelial lesion (LSIL), high-grade squamous intraepithelial lesion (HSIL), and squamous cell carcinoma (SCC) categories.

Results

The integrated Autostain-PAP and Autoscope platform enables standardized smear preparation, automated slide digitization, and AI-assisted pre-classification of cervical cytology slides. The AI model supports rapid identification of clinically relevant and suspicious cells, allowing pathologists to focus on abnormal regions rather than exhaustive manual screening. This workflow significantly reduces screening time and workload while maintaining expert oversight. The system architecture supports remote review, enabling centralized pathology expertise to serve multiple decentralized laboratories.

Conclusions / Implications for Practice or Policy

This integrated, low-cost, AI-assisted cytology platform addresses critical gaps in India’s cervical cancer screening ecosystem by enabling scalable deployment in tier-2 and tier-3 settings. By reducing infrastructure requirements and pathologist burden, the approach has the potential to expand screening coverage, improve turnaround times, and support national cervical cancer control and elimination efforts. The platform may inform policy initiatives aimed at decentralizing cervical cancer screening and strengthening diagnostic capacity across underserved regions.

Figure 1. Automated cytology workflow showing Autostain-PAP slide preparation and Autoscope digitization with AI-assisted cell classification and pathologist review.