What question did this study set out to answer?

To evaluate the clinical diagnostic value of transvaginal color Doppler ultrasound combined with an improved mean shift artificial intelligence segmentation algorithm for idiopathic premature ovarian failure.

May 25, 2022Computational and Mathematical Methods in MedicineOpen Access

Diagnosis of Idiopathic Premature Ovarian Failure by Color Doppler Ultrasound under the Intelligent Segmentation Algorithm

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Why the study?

The application value of transvaginal color Doppler ultrasound using an improved mean shift algorithm for diagnosing idiopathic premature ovarian failure needed evaluation.

Does transvaginal color Doppler ultrasound with an improved mean shift algorithm improve the evaluation of idiopathic premature ovarian failure?

Population

80 patients with idiopathic POF and 40 healthy control volunteers

Comparison

Idiopathic POF patients vs healthy control volunteers

Design

Case-control study

Authors

LYLanlan YuNorth Sichuan Medical University XQXiaofeng QingHuaian First People’s Hospital

Discussion

Member takes

Implication

May support algorithm-enhanced ultrasound for POF diagnosis; leaves open validation in prospective trials.

Key Points

To evaluate the clinical diagnostic value of transvaginal color Doppler ultrasound combined with an improved mean shift artificial intelligence segmentation algorithm for idiopathic premature ovarian failure.
Enrolled 80 patients with idiopathic premature ovarian failure and 40 healthy control volunteers undergoing transvaginal color Doppler ultrasound examination.
Implemented an improved mean shift AI algorithm for ovarian ultrasound image segmentation, benchmarking against Snake, Live_wire, and standard mean shift models.
Measured ovarian dimensions and evaluated ovarian artery hemodynamic parameters, including peak systolic velocity, end-diastolic velocity, resistance index (RI), and pulsatility index (PI).
The improved AI segmentation achieved a significantly lower relative difference degree and significantly higher relative overlap degree and Dice coefficients than comparison algorithms (P < 0.05).
Ovarian mediolateral diameter (2.11 ± 0.36 cm vs. 2.87 ± 0.31 cm) and anteroposterior diameter (1.13 ± 0.34 cm vs. 1.86 ± 0.28 cm) were significantly smaller in the experimental group than in controls (P < 0.05).
Ovarian artery detection rates were significantly reduced, and vascular resistance indices—specifically PI, RI, and systolic-to-diastolic flow ratios—were significantly higher in patients with premature ovarian failure than controls (P < 0.05).

Structured PICO

Does transvaginal color Doppler ultrasound with an improved mean shift algorithm improve the evaluation of idiopathic premature ovarian failure?

Population

120 women, including 80 patients with idiopathic premature ovarian failure (POF) and 40 healthy volunteers.

Intervention

Transvaginal color Doppler ultrasound using an improved mean shift algorithm based on artificial intelligence.

Comparator

Healthy volunteers (for clinical parameters) and traditional segmentation algorithms including Snake, Live_wire, and traditional mean shift (for image processing).

Outcome

Algorithm segmentation performance (relative difference degree, relative overlap degree, Dice coefficient) and ovarian artery parameters (peak systolic flow rate, diastolic flow rate, resistance index, and pulsatile index).surrogate

Transvaginal color Doppler ultrasound using an AI-based improved mean shift algorithm can effectively evaluate ovarian functional status and hemodynamics in patients with premature ovarian failure.

Cite This Study

Yu et al. (2022) studied this question.

synapsesocial.com/papers/6a90ec638b76b10fb073ce7b https://doi.org/10.1155/2022/2645607

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Diagnosis of Idiopathic Premature Ovarian Failure by Color Doppler Ultrasound under the Intelligent Segmentation Algorithm

Discussion

Member takes

Implication

Key Points

Structured PICO

Cite This Study

Diagnosis of Idiopathic Premature Ovarian Failure by Color Doppler Ultrasound under the Intelligent Segmentation Algorithm

Discussion

Member takes

Implication

Key Points

Structured PICO

Cite This Study

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