AI-driven breast cancer detectionAI that reads every mammogram.

AVO detects masses and calcifications, scores each finding for malignancy, and compares it against the patient's earlier studies — a second read on every screening mammogram, in under three minutes.

Get in touch

The problem

Every fourteen seconds, a woman is diagnosed with breast cancer.

Diagnosed since you opened this page

94 countries out of 194 have a screening programme.

0 of 194 countries screen

AVO makes a second read cheap enough to scale — including into mobile units, at the point of screening.

A second read can take three weeks.

Days to a second read

Day 1 — AVO, same session Day 21+ — conventional second read

~50

Scans a radiologist reads in a day. Demand is rising; capacity is not.

10–30%

Of breast cancers may be missed — higher in dense tissue.

A consistent, fatigue-free second read, in under three minutes.

Reported miss rates, reading volumes and review waits from published screening literature.

What the AI doesSix things the model does to every study.

Mass detection

Finds suspicious masses and marks them on the image itself, where the radiologist is already looking.

Calcification detection

Picks out microcalcification clusters, including in the dense tissue where they are hardest to see.

Malignancy scoring

Gives every finding a probability of malignancy, and the study a cumulative risk score out of ten.

Temporal comparison

Puts this year's findings beside the previous mammograms, so what changed is visible rather than remembered.

Worklist prioritisation

Thousands of screening studies ordered by risk, so the queue reflects urgency instead of arrival.

Learning loop

Radiologist feedback returns to the model, so it keeps improving on the population it actually reads.

A batch of processed studies in AVO, each with a risk score, the
abnormalities found and a classification. — A batch as the model leaves it: every study scored, with what it found and how it reads.

A second read in under three minutes, inside the radiology workflow you already have.

What comes backOne study, read and scored.

Sample outputRead time 2m 40s

Cumulative risk spectrum score

9.4/ 10

Finding: Suspicious mass, suspicious calcifications
Malignancies: 2 found
Flag: Priority review

A mammogram in the AVO viewer. One region is circled, magnified beside the
image, and labelled 08 Mass, Suspicious. — The same finding in the viewer: circled on the image, magnified, and labelled where the radiologist is already looking.

Clinical validationValidated on real patients.

AVO has concluded a clinical study with its partner hospital, validating real-world performance on real patients. Models continue to improve through a feedback loop with reviewing radiologists.

1,300+

Real-world patients reviewed

3,000+

Mammograms annotated by trained radiologists

JCI

Accredited partner hospital

Who's behind it

From blueprint to operating system.

Builds AI systems that operate inside enterprises — securely, at scale, and with measurable performance.

30+ years of cancer treatment and oncology data.

Shaukat Khanum Memorial Cancer Hospital & Research Centre

Through its cancer registry since 1994. One of Pakistan's most advanced cancer care ecosystems, JCI Enterprise re-accredited.

Mammography on wheelsScreening that goes to the village.

Across the world, millions of women don't know about mammography, or have no way of reaching it. AVO goes to them first: a mobile unit screens women in their own village, AVO reads each mammogram on board in under three minutes, and the serious cases are referred on to a hospital.

Get in touchIf this is the problem you have, we would like to hear from you.

info@avolabs.ai

Or find AVO on LinkedIn.

Please don't send patient data or images by email.