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Imaging Studies II: Ultrasonography01:24

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IntroductionUltrasonography, or renal ultrasound, is a noninvasive medical imaging technique that uses high-frequency sound waves to visualize the kidneys, ureters, bladder, and surrounding tissues.Indications for Urinary System UltrasonographyUrinary system ultrasonography is indicated in various clinical scenarios, such as:Kidney Stones (Urolithiasis): To detect and monitor the size and presence of kidney or urinary tract stones.Hydronephrosis: To assess the dilation of the renal pelvis and...
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Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
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Kidney, Ureter, and Bladder (KUB) StudiesKidney, Ureter, and Bladder (KUB) studies are standard diagnostic imaging procedures used to assess the anatomy of the urinary system. They are commonly utilized for patients experiencing abdominal pain or urinary symptoms. By using a simple X-ray of the abdomen, KUB studies can reveal structural and pathological abnormalities within the kidneys, ureters, and bladder. These studies are particularly valuable in diagnosing kidney stones, urinary...
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Predicting obstruction risk using common ultrasonography parameters in paediatric hydronephrosis with machine

Adree Khondker1,2, Jethro C C Kwong3,4, Margarita Chancy2

  • 1Temerty Faculty of Medicine, University of Toronto, Toronto, Onterio, Canada.

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|August 18, 2023
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Summary

Machine learning models using routine ultrasound findings can predict renal obstruction risk in children with hydronephrosis, helping to safely avoid unnecessary diuretic renography. This approach improves diagnostic criteria for pediatric renal obstruction.

Keywords:
PUJ obstructionartificial intelligencediuretic renogramhydronephrosismachine learningnuclear medicine

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Area of Science:

  • Pediatric Urology
  • Medical Imaging
  • Machine Learning in Healthcare

Background:

  • Diuretic renography is a standard tool for assessing renal obstruction in pediatric hydronephrosis.
  • Routine ultrasonography (US) findings are often used, but predicting obstruction risk can be challenging.
  • There is a need for improved criteria to identify children who can safely avoid diuretic renography.

Purpose of the Study:

  • To develop a machine learning model using routine US findings to predict renal obstruction risk.
  • To establish safe criteria for deferring diuretic renography in pediatric patients with isolated hydronephrosis.
  • To compare the model's performance against the Society for Fetal Urology (SFU) grade.

Main Methods:

  • A retrospective analysis of 304 patients with isolated hydronephrosis who underwent renal US and diuretic renography.
  • Data abstracted included patient demographics and routine US findings (laterality, kidney length, anteroposterior diameter, SFU grade).
  • A Random Forest model, termed 'Artificial intelligence Evaluation of Renogram Obstruction' (AERO), was trained to classify obstruction risk.

Main Results:

  • The AERO model achieved a binary area under the receiver-operating-characteristic curve (AUROC) of 0.84, outperforming the SFU grade.
  • External validation demonstrated a binary AUROC of 0.76, with age, anteroposterior diameter, and SFU grade as key predictive features.
  • At a 30% probability threshold, AERO could allow 66 more patients per 1000 to safely avoid renography compared to routine SFU Grade ≥3 criteria.

Conclusions:

  • Routine ultrasonography findings, when combined with machine learning, can enhance criteria for safely deferring diuretic renography in children with isolated hydronephrosis.
  • The developed AERO model shows promise in improving the selection of patients for diuretic renography.
  • Further optimization and validation are necessary before widespread clinical implementation.