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The diagnostic gap
we are working to close

Millions of women in India and Southeast Asia are diagnosed late — or not at all — because access to quality cancer diagnostics is scarce, slow, and unequal. We are building the tools to change that.

Background Cervical Cancer Breast Cancer Endometrial Cancer
The Context

A system under critical pressure

Cancer diagnosis relies heavily on manual interpretation by experienced pathologists. But there is a global shortage — estimated at 1 pathologist per 15,000–20,000 people even in high-income countries. In low- and middle-income countries like India, the ratio falls to roughly 1 per 100,000 people.

At the same time, cancer incidence continues to rise, with millions of new cases annually. This growing mismatch between demand and capacity leads to diagnostic delays, inconsistent quality, and preventable deaths.

For women's cancers — cervical, breast, and endometrial — early detection can reduce mortality by 30 to 70 percent. Yet many cases are diagnosed late because quality diagnostics are simply not accessible where patients live.

Digital pathology adoption remains low across much of the region, largely due to the high cost of existing solutions from large multinational companies. There is a critical need for scalable, affordable, and AI-assisted diagnostic tools that work in real-world healthcare settings.

1 : 100,000+
Pathologist-to-patient ratio in India and comparable LMICs
vs. 1 : 15,000–20,000 in high-income countries
30–70%
Reduction in mortality possible with early detection of women's cancers
Cervical, breast, and endometrial cancers combined
Millions
Of new cancer cases annually — increasing diagnostic workload and delay
Global incidence continues to rise year on year
01

Cervical Cancer

The most preventable cancer that still claims hundreds of thousands of lives annually — because screening doesn't scale.

600k+
New cases globally each year
90%
Of deaths in LMICs
75,000
Annual deaths in India alone
Pap smear cytology — cervical cells under microscope

Pap smear cytology — abnormal cervical cells under microscope

The Scale of the Problem

Cervical cancer remains one of the leading causes of cancer-related deaths among women worldwide. More than 90% of these deaths occur in low- and middle-income countries, reflecting deep inequities in access to early detection and care. In India alone, cervical cancer claims over 75,000 lives annually — even though it is largely preventable and highly treatable when detected at an early stage.

"Despite the proven effectiveness of Pap smear-based screening, coverage remains critically low due to systemic challenges that AI can directly address."

Why Current Screening Fails to Scale

  • Severe shortage of trained cytotechnologists and pathologists to review slides
  • Manual slide review is slow, error-prone, and subject to significant inter-observer variability — false-negative rates of up to 30% for pre-cancerous lesions
  • Poor access to quality laboratories in rural and underserved regions
  • Fragmented lab workflows, long turnaround times, and lack of digital tracking delay diagnosis and follow-up
  • Traditional screening methods cannot efficiently scale to millions of women periodically — the essential requirement for effective population-level prevention
02

Breast Cancer

The most commonly diagnosed cancer in women worldwide — and among the most diagnostically complex tasks in routine pathology.

2.3M
Women affected globally per year
High
Inter-observer variability in grading
Multiple
Slides per patient, each requiring expert review
Breast histopathology H&E slide

Breast tissue histopathology — H&E stained section

Biological Complexity

Breast cancer diagnosis is challenging due to the biological complexity and heterogeneity of breast tissue. A single specimen may contain benign changes, pre-invasive lesions such as ductal carcinoma in situ (DCIS), invasive carcinoma, inflammation, and fibrosis. Critical distinctions — like atypical hyperplasia versus low-grade DCIS, or identifying microinvasion — are often extremely subtle and further complicated by sampling limitations, particularly in core needle biopsies.

Subjectivity and Workload Pressure

  • Tumor grading, mitotic counts, and nuclear atypia assessment are semi-quantitative and prone to inter-observer variability, even among experienced pathologists
  • Rising case volumes, multiple slides per patient, and extensive reporting requirements add time pressure and fatigue
  • Risk of missing rare but clinically important findings such as lymphovascular invasion or micrometastases
  • Interpretation of IHC markers (ER, PR, HER2, Ki-67) is sensitive to pre-analytical factors and often yields borderline results with direct treatment implications

"Digital pathology introduces very large image files and navigation challenges — together making breast cancer diagnosis one of the most demanding tasks in routine pathology practice."

03

Endometrial Cancer

A gynaecologic malignancy on the rise — where subtle morphological differences and molecular complexity make early, accurate diagnosis exceptionally difficult.

400k+
Women diagnosed globally each year
Rising
Incidence due to ageing and obesity
Subtle
Morphological differences in early-stage disease
Endometrial histology — glandular tissue

Endometrial histology — glandular architecture

A Broad Morphologic Spectrum

From a pathology perspective, endometrial cancer diagnosis is challenging because of the broad morphologic spectrum and overlap with benign and premalignant conditions. Pathologists must carefully distinguish normal endometrium, hyperplasia, atypical hyperplasia (EIN), and well-differentiated endometrioid carcinoma — often using small biopsy or curettage samples that may not represent the entire lesion.

Subtle differences in gland architecture and cytologic atypia, combined with sampling limitations, can make early or low-grade cancers particularly difficult to identify with confidence.

Molecular Integration Adds Complexity

  • Assessing depth of myometrial invasion, lymphovascular space invasion, and tumour grade — all semi-quantitative and subject to variability, yet directly influencing treatment decisions
  • Modern classification increasingly relies on IHC and molecular markers (p53, mismatch repair proteins, POLE status) — requiring integration of morphology with biomarker data
  • Standardised synoptic reporting demands add cognitive load alongside rising digital pathology workloads

"Together with rising workloads and the transition to digital pathology, endometrial cancer diagnosis is a cognitively demanding and high-stakes task in routine practice."

Our Response

See how Manalife AI addresses these challenges

ManaScan and Pathora are designed specifically to tackle the diagnostic bottlenecks across cervical, breast, and endometrial cancer pathways.

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