Artificial intelligence in perineal rehabilitation: current situation, challenges and prospects

cjouanneau
3 days ago
4 min read

Artificial intelligence (AI) is now part of many areas of healthcare: imaging, triage, signal interpretation, functional rehabilitation, decision support tools, etc. But what about a very specific field, that of perineal rehabilitation?

More and more healthcare professionals are asking themselves: has AI already become part of our practices? Are there any concrete solutions? Is innovation relevant for our patients - women?

Here is an objective and up-to-date overview of the state of AI applied to the perineum, without jargon, but without trivialising the complexity of the subject.

Why talk about AI in pelvic floor rehabilitation?

Pelvic floor rehabilitation is based on well-established principles:

motor learning,
selective contraction,
regular practice,
feedback,
progressive adaptation,
and of course clinical expertise.

Yet several limitations persist in daily practice:

1. The difficulty some patients have in perceiving and controlling pelvic floor muscles

Studies show that many women do not perform a correct pelvic floor contraction without professional guidance. Motor learning of the pelvic floor is complex, sometimes abstract, and verbal or tactile feedback may not be enough.

2. An evaluation that can sometimes be subjective

Even with experience, pelvic floor assessments can vary from one practitioner to another. Invasive tools (probes) provide more objective feedback, but they are not suitable for all patients.

3. A follow-up between sessions that is hard to guarantee

Motivation, consistency, execution quality… many factors influence the final outcome.

This is where AI becomes relevant: it can improve contraction analysis, enhance immediate feedback, standardize certain measurements, and facilitate remote monitoring.

Where does the scientific research stand?

Although the field is still young, several research areas are already active.

1. AI applied to pelvic floor imaging

Some teams have worked on models capable of automatically segmenting the pelvic hiatus, measuring organ mobility, or analyzing pelvic floor dynamics using 2D or 3D ultrasound.

Preliminary results show:

reduced analysis time,
reduced inter-operator variability,
strong potential for objective initial assessment.

These advances are promising but not yet widely integrated into clinical practice.

2. AI to detect or classify pelvic floor contractions

Several publications describe algorithms capable of:

detecting a pelvic floor contraction,
analyzing its quality (strength, hold, release),
classifying signals in real time.

These models are common in biomedical research (EMG, sensors, ultrasound), but are rarely integrated into ready-to-use clinical devices.

3. AI-enhanced telerehabilitation

With the rise of telehealth, researchers are exploring:

automated movement analysis,
interpretation of sensor-based signals,
error detection,
alerts in case of poor execution.

These approaches aim to make at-home practice safer, but still rely on validated, reliable sensors.

Overview of existing devices: AI or no AI?

Today, three main categories of pelvic floor rehabilitation technologies can be identified:

1. “Classic” biofeedback devices (without AI)

Connected probes, internal or external sensors, mobile apps.They provide visual or auditory feedback, but interpretation remains basic: amplitude, rhythm, EMG curve…

These tools are useful, but do not rely on intelligent signal interpretation.

2. Sensors + app + “assisted” analysis

These solutions process signals better but do not yet include full AI algorithms.They support home training and improve motivation, but analysis quality depends heavily on the hardware.

3. Devices integrating real-time embedded AI

Here, AI is not just displaying curves:it interprets, classifies, detects, filters, differentiates — and sometimes corrects — in real time.

This is by far the most innovative category… and still the rarest today.

The French example: Blueback among emerging AI-enhanced solutions

In this landscape, France has very few actors offering real-time embedded AI dedicated to pelvic floor rehabilitation. Among them, Blueback (near to Paris) stands out as a French innovation based on a patented technology, Deep EMG®, which allows:

the use of non-invasive surface sensors,
access to deep muscle activity,
and real-time AI interpretation (classification, detection, filtering).

This solution, called Inner UP - pelvic-floor biofeedback without probe - has been rrewarded by the French Minister of Research in 2024 (Winner of the innovation national contest).

This approach sits at the crossroads of biomechanics, real-time AI, and biofeedback.

It is not a “100% AI device,” and it does not replace the therapist — it provides objective, coherent insights to enrich the session while remaining within the framework of a class I medical device.

Potential clinical benefits:

objective measurement of contractions,
partial standardization of assessment,
improved patient feedback,
better understanding of movement,
non-invasive analysis.

As with any device, use must remain supervised, contextualized, and complementary to clinical expertise.

Can AI really change your practice?

Here are the benefits most frequently cited by therapists using intelligent solutions:

1. Immediate feedback that patients understand

AI can say more than “the curve goes up or down”:it can differentiate a true contraction, a compensatory effort, poor posture, or incomplete relaxation.

2. Standardization and objectivity

Evaluation becomes less dependent on manual methods or subjective perception.This reassures some patients and simplifies monitoring.

3. Increased motivation

Patients can see their progress, which boosts adherence — a key success factor in pelvic floor rehabilitation.

4. Possibility of remote follow-up

Some technologies allow safe, guided practice between sessions.

Limitations and precautions

Even though AI opens many possibilities, it does not replace clinical reasoning.

Some patients prefer to avoid connected tools (age, digital comfort, intimacy).
AI depends on data quality: poorly placed sensors = incorrect interpretation.
Devices must comply with regulations (GDPR, medical device rules, data security).
AI has no awareness of context, pain, posture, or patient history.
Clinical studies remain limited in size and number.

In short: AI enriches the session but never replaces it.

Conclusion: AI is already here… but still in its early stages

Pelvic floor rehabilitation is gradually entering the digital era. AI is not yet widespread, but it is already present in:

imaging,
contraction detection,
advanced biofeedback,
connected devices,
and some tools with real-time analysis.

France is not lagging behind:several innovations are emerging, including Blueback, which applies real-time, non-invasive AI to pelvic floor assessment.

AI will certainly evolve, but its role will remain clear: to assist practitioners, not replace them.

Sources (short, neutral selection)

Bø et al., Pelvic Floor Muscle Training Evidence Review, Journal of Women’s Health.
Ashton-Miller & DeLancey, The Functional Anatomy of the Pelvic Floor, Obstetrics & Gynecology.
Hung et al., Automated Pelvic Floor Ultrasound Analysis Using Deep Learning, Scientific Reports.
Reimers et al., Digital Therapeutics for Pelvic Floor Disorders, JAMA.
Blueback, public technical documentation on Deep EMG® (official website).
ICS (International Continence Society), abstracts related to AI and PFMT.