Using AI Vision to Improve Reproductive Health Outcomes

How MDC applies computer vision to reduce variability, improve consistency, and deliver clearer fertility decisions

When outcomes depend on how well we measure

Reproductive medicine is outcome driven. Yet many fertility decisions still rely on measurements that vary between observers, laboratories, and even days. Semen analysis is a clear example. Small differences in interpretation can change:

• Whether treatment is recommended
• When intervention begins
• How progress is judged over time

At Marylebone Diagnostic Centre (MDC), we adopted SCA vision not to replace clinical expertise, but to stabilise measurement, strengthen confidence, and ultimately improve patient outcomes. This article explains how SCA system vision works in fertility diagnostics, why it matters, and how MDC uses it responsibly within a clinician-led model.

The hidden problem in fertility testing: variability

Most fertility tests are technically correct. The issue lies in consistency.

Where variability enters the system

Even in excellent laboratories, these factors introduce noise. Over time, noise becomes uncertainty. For patients tracking fertility or preparing for IVF, uncertainty is costly.

What is SCA vision in diagnostics?

SCA system vision refers to computer systems that can:

• Identify objects
• Track movement
• Measure shape and behaviour
• Convert visual information into structured data

In semen analysis, this means software can:

• Detect individual sperm cells
• Track their movement across frames
• Quantify speed and trajectory
• Assess morphology patterns

Instead of a snapshot impression, AI vision creates a measurable record.

Why MDC adopted SCA vision for semen analysis

MDC’s decision was outcome-focused, not technology-led. The goals were clear:

• Reduce observer variability
• Improve repeatability
• Enable trend analysis
• Strengthen patient explanations

Manual vs AI-assisted assessment

The technology adds stability. The clinician adds meaning.

The role of the laboratory scientist remains central

SCA AI vision does not make clinical decisions. It provides cleaner inputs. At MDC:

• All semen samples are prepared and handled by trained professionals.
• SCA AI outputs are reviewed, validated, and contextualised.
• Final reports remain clinician reviewed.

This safeguards quality while improving measurement integrity.

From measurement to outcomes: why consistency matters

Consistency changes outcomes in three key ways.

1. Earlier signal detection

Small declines in motility or morphology often precede clinical thresholds.

2. Better monitoring of interventions

Lifestyle changes, medical treatments, or fertility optimisation plans require feedback. AI vision allows MDC to compare like-for-like results over time.

Patients see progress. Clinicians gain confidence.

3. Improved decision-making for fertility pathways

Fertility decisions are binary only on the surface. In reality, they depend on trajectory. SCA AI vision supports:

• IVF timing decisions
• Repeat testing justification
• Avoidance of unnecessary escalation

Outcomes improve when decisions align with data quality.

SCA vision and DNA fragmentation

DNA fragmentation reflects sperm genetic integrity. While measured separately, interpretation improves when combined with consistent semen metrics.

Integrated interpretation

The importance of pairing vision with hormones

Semen behaviour reflects endocrine signalling. At MDC, AI-assisted semen analysis is routinely interpreted alongside:

• Testosterone
• FSH
• LH
• SHBG
• Prolactin

Example correlation

Machine vision improves the reliability of these correlations.

Urine diagnostics: supporting reproductive insight

Urine testing is often overlooked in fertility discussions. At MDC, urine diagnostics help explain AI-detected changes.

SCA vision flags the change. Urine data helps explain why.

PSA, ageing, and outcome planning

SCA vision also plays a role in long-term male health planning. PSA is not a fertility test, but it informs:

• Age-related risk assessment
• Baseline health context
• Longitudinal monitoring

When semen trends change with age, PSA provides additional biological framing.

What patients experience differently

Patients do not experience “AI vision”. They experience clarity. Common feedback includes:

• Clearer explanations of results
• Confidence in repeat testing
• Better understanding of change over time
• Reduced anxiety from ambiguous language

Technology improves outcomes when it improves understanding.

What fertility clinics gain

For partner clinics, MDC’s AI-assisted diagnostics offer:

• Consistent input data
• Reduced repeat sampling
• Stronger justification for pathway decisions
• Improved patient trust

Diagnostics become a decision support layer, not a bottleneck.

Safety, governance, and responsible use

MDC applies SCA vision within strict boundaries:

• No autonomous reporting
• No unsupervised outputs
• Full clinical accountability

Technology supports care. It does not direct it.

Why SCA vision matters in 2026

Healthcare is moving toward:

• Standardisation
• Longitudinal data
• Predictive insight
• Evidence-based personalisation

Reproductive medicine cannot remain visually subjective while the rest of diagnostics advances. SCA vision bridges that gap.

Final thoughts

SCA vision does not make fertility medicine impersonal. It makes it more precise. By reducing variability, improving consistency, and enabling trend-based insight, MDC uses AI vision to support better reproductive outcomes – without removing clinical judgement.

This is not automation for speed. It is measurement done properly.

Related MDC services

• Advanced semen analysis (SCA-assisted)
• DNA fragmentation testing
• Male fertility hormone profiles
• Urine health screening
• PSA baseline monitoring

Marylebone Diagnostic Centre – Central London
Same-day testing • Clinician-reviewed diagnostics • Data-driven fertility care
73 Baker Street, London W1U 6RD
+44 7495 970109
Monday–Saturday | 08:00–16:00
5-minute walk from Baker Street tube