Behind the Paper

IntelligynAI-FS: Clinical Feasibility of AI Support in Ultrasound Assessment of Ovarian Tumors

This clinical study investigates the potential of AI support in ultrasound assessment of ovarian tumors, aiming to improve diagnostic accuracy, reduce unnecessary interventions, and optimize limited healthcare resources for better patient outcomes.
Filip Christiansen Oct 13, 2025

Ovarian Cancer: Diagnostic Challenges and Current Practices

Ovarian cancer is the most lethal gynecologic malignancy, with a five-year survival rate below 50%, primarily due to frequent late-stage diagnoses.1 Benign adnexal masses, common across all age groups, are often asymptomatic and typically detected incidentally during imaging for other purposes.2 Nearly 10% of asymptomatic postmenopausal women have an ovarian lesion, though only 1% are malignant.3 Managing these patients presents significant diagnostic challenges, requiring accurate triage to balance the risks of missing malignancies against unnecessary surgeries.

Conservative management of benign lesions through ultrasound follow-up or, if symptomatic, minimally invasive surgery, reduces morbidity, preserves fertility, and avoids unnecessary healthcare costs.4,5 In contrast, women with suspected ovarian cancer benefit from referral to gynecologic oncology centers, where specialized surgical management increases the likelihood of complete tumor removal, improving survival outcomes.6,7,8

Transvaginal ultrasound is the primary technique used to distinguish benign from malignant ovarian lesions due to its accessibility and high diagnostic accuracy, especially when performed by specialist examiners.9,10 However, a shortage of experienced examiners, particularly in underserved areas, limits access to timely and accurate diagnoses, leading to delays in treatment and increased risks of unnecessary surgical interventions.11,12

Advancing Ultrasound Diagnostics with AI Support

The increasing shortage of specialist ultrasound examiners has created significant challenges in the timely and accurate diagnosis of ovarian lesions, resulting in treatment delays and unnecessary surgeries. This highlights the need for innovative approaches to support medical professionals in delivering high-quality care. Our research team, based at the Karolinska Institute and the Science for Life Laboratory (SciLifeLab), in collaboration with KTH Royal Institute of Technology, the Stockholm South General Hospital (Södersjukhuset), and Intelligyn—a company specializing in AI-driven diagnostic tools—is exploring the potential of artificial intelligence (AI) in enhancing the diagnosis of ovarian tumors.

Recent advancements in AI, particularly deep learning, have shown remarkable promise in medical imaging diagnostics, achieving performance comparable to medical specialists in areas such as dermatology and breast cancer screening.13,14 Building on this progress, our earlier work demonstrated that deep learning models can accurately distinguish between benign and malignant ovarian tumors, matching the diagnostic accuracy of experienced ultrasound examiners.15

In a recent large-scale international validation study in Nature Medicine, we validated these findings across diverse populations and ultrasound equipment.16 This study included 3,652 patients from 20 centers in 8 countries and the AI model consistently outperformed 66 ultrasound examiners, including 33 specialists with a median of 17 years of experience. Additionally, a retrospective simulation demonstrated the potential of the AI model to serve as a second independent reader, supporting non-specialist examiners in a clinical triage workflow. In this setup, specialists reviewed only cases of disagreement, leading to a 63% reduction in referrals to specialists and an 18% reduction in incorrect diagnoses. These findings highlight the potential of diagnostic AI support to significantly enhance efficiency and optimize the use of limited healthcare resources.

IntelligynAI-FS: Evaluating Real-World Feasibility

Building on this foundation, our upcoming clinical feasibility and performance study, IntelligynAI-FS, aims to evaluate how integrating AI support into clinical workflows affects examiners’ confidence and decision-making in the diagnosis of ovarian lesions. Through this work, we hope to further demonstrate AI’s potential to improve diagnostic accuracy, streamline workflows, and ultimately enhance patient outcomes.

Study Overview and Objectives

IntelligynAI-FS is a pre-market exploratory clinical investigation with the primary aim to evaluate the clinical feasibility of using AI as a real-time decision support tool during ultrasound examinations of ovarian lesions. Specifically, it explores:

  • How AI integration influences examiners’ workflows and work environments.
  • Whether AI support enhances examiners' confidence in their diagnoses and management decisions.
  • The ability of examining physicians to capture and select images suitable for AI analysis.
  • The degree to which AI-supported assessments align with outcomes from surgery or extended ultrasound follow-ups.

Study Design and Methodology

This single-center observational study will be conducted at Södersjukhuset in Stockholm, Sweden. The study design focuses on the practical integration of IntelligynAI, an AI-driven diagnostic support tool, into clinical practice. During gynecological ultrasound examinations, examiners will send selected images to the IntelligynAI platform, which will return a report containing a cancer risk prediction and a suggested management approach. The platform will be hosted on a local server within the hospital’s firewall during the study but may also be hosted in the cloud in the future. The platform can be accessed either directly from the ultrasound system or from the examiner’s workstation.

With the use of two discriminatory thresholds for the risk prediction, tumors are categorized as benign, inconclusive (difficult to classify), or malignant. The thresholds have been carefully selected to ensure high sensitivity and specificity for conclusive cases, while keeping the rate of inconclusive classifications below 20%.

The IntelligynAI-FS study will be preceded by a “Clinical Usability and Safety” study, a summative human factors engineering investigation where the workflow and user experience will be evaluated. At least 15 examiners will test the system in a controlled environment using retrospective ultrasound images. Participating physicians will receive targeted training on the AI tool’s functionality and guidance on optimizing image acquisition to ensure safe and effective use during clinical examinations. By incorporating these preparatory steps, the “Clinical Usability and Safety” study aims to facilitate the seamless integration of the AI tool into real-world workflows during the subsequent IntelligynAI-FS study, while proactively addressing potential usability challenges.

Eligibility and Recruitment

Eligible participants include women aged 18 and older with newly detected adnexal lesions. Recruitment began in February 2025 and aims to enroll approximately 60 participants over the course of the study.

Expected Outcomes and Impact

This study aims to explore the benefits and limitations of integrating AI-driven support into real-world clinical practice. A successful integration could lead to:

  • Enhanced diagnostic confidence, reducing unnecessary referrals and surgeries, while lowering patient anxiety, fertility loss, and morbidity.
  • Improved work environments for examiners, with reduced workloads and stress.
  • Significant healthcare cost savings through optimized resource use and improved diagnostic accuracy.
  • Improved patient outcomes, including higher survival rates, reduced complications, and better quality of life.

However, we recognize that the use of AI in clinical settings carries certain risks, including the potential for discrepancies between AI assessments and final diagnoses. In our study, the AI’s output is used solely as an advisory tool, with the ultimate responsibility for patient care resting with the attending physician.

Ethical Approvals

This study has obtained necessary ethical approvals from the Swedish Ethics Review Authority (Etikprövningsmyndigheten) (Dnr 2024-03312-01) and the Swedish Medical Products Agency (Läkemedelsverket) (Dnr 5.1-2024-28221), ensuring compliance with high ethical standards, including informed consent and participant safety.

Looking Ahead

As we progress, the insights gained from IntelligynAI-FS will contribute to refining AI tools for broader clinical use. Our hope is to pave the way for more accessible, accurate diagnostic support across diverse medical settings. The findings from this study will be disseminated in peer-reviewed publications.

Other Ongoing and Planned Studies

In addition to IntelligynAI-FS, several other studies are underway or planned to further explore the impact of AI in ovarian tumor diagnostics:

  • Retrospective Multi-Reader, Multi-Case (MRMC) Study: This study will involve approximately 30 examiners with different levels of expertise assessing at least 500 ovarian lesions twice, once with and once without AI support, to evaluate the impact of AI support on diagnostic accuracy and inter-observer variability.
  • Prospective Multicenter Study (OV-AID Phase I): This ongoing study, which began in February 2021, compares the AI model stand-alone with the clinical assessment of examining physicians, with examiners blinded to the AI predictions. Read more here: ISRCTN88222986.
  • Randomized Controlled Multicenter Study (OV-AID Phase II): Planned to start in 2026, this study will explore the effect of AI support on patient outcomes, management, and cost-benefit measures in ultrasound assessment of ovarian lesions.

Acknowledgments

This study is made possible through the support of the Swedish Research Council (Vetenskapsrådet), the Swedish Cancer Society (Cancerfonden), the Stockholm Regional Council (Region Stockholm), the Cancer Research Funds of Radiumhemmet (Radiumhemmets Forskningsfonder), the Swedish Agency for Innovation Systems (Vinnova), and Medtech4Health.

Join Us on Our Journey

For those interested in learning more about IntelligynAI-FS and staying updated on our progress, we encourage you to visit our ISRCTN registry entry and follow Intelligyn's website and our forthcoming publications. Together, we can move closer to ensuring that innovative AI solutions meet the needs of both patients and healthcare providers.

References

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  2. American College of Obstetricians and Gynecologists. Practice bulletin no. 174: evaluation and management of adnexal masses. Obstet Gynecol, 128:e210–e226 (2016). DOI: 10.1097/aog.0000000000001768.
  3. Sharma, A. et al. Risk of epithelial ovarian cancer in asymptomatic women with ultrasound‐detected ovarian masses: a prospective cohort study within the UK collaborative trial of ovarian cancer screening (UKCTOCS). Ultrasound Obstet Gynecol, 40:338–344 (2012). DOI: 10.1002/uog.12270.
  4. Yazbek, J. et al. Effect of quality of gynaecological ultrasonography on management of patients with suspected ovarian cancer: a randomised controlled trial. Lancet Oncol, 9:124–131 (2008). DOI:10.1016/s1470-2045(08)70005-6.
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  11. Van Holsbeke, C. et al. Ultrasound experience substantially impacts on diagnostic performance and confidence when adnexal masses are classified using pattern recognition. Gynecol Obstet Invest,69:160–168 (2010). DOI: 10.1159/000265012.
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  16. Christiansen, F. et al. International multicenter validation of AI-driven ultrasound detection of ovarian cancer. Nat Med, 31:189–196 (2025). DOI: 10.1038/s41591-024-03329-4.