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        <datestamp>2026-09-29T17:25:15Z</datestamp>
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          <dc:title>&lt;p&gt;Performance metrics utilised across the studies (Matthews Correlation Coefficient (MCC), Fowlkes-Mallows Index (FMI), Area Under Curve (AUC), Receiver Operating Curve (ROC), Dice Similarity Coefficient (DSC), Jaccard Index (JI), Intersection Over Union (IOU), Average Distance (AD), Average Surface Distance (ASD), Average Symmetric Surface Distance (ASSD).&lt;/p&gt;</dc:title>
          <dc:creator>Sinling Tiffany Yu (25138201)</dc:creator>
          <dc:creator>Zahra Amini (2034757)</dc:creator>
          <dc:creator>Shereen Fouad (4633552)</dc:creator>
          <dc:creator>Jan Novak (156878)</dc:creator>
          <dc:creator>Antonio Fratini (3373664)</dc:creator>
          <dc:subject>Medicine</dc:subject>
          <dc:subject>Biotechnology</dc:subject>
          <dc:subject>Developmental Biology</dc:subject>
          <dc:subject>Cancer</dc:subject>
          <dc:subject>Science Policy</dc:subject>
          <dc:subject>two assessed inter</dc:subject>
          <dc:subject>prior systematic review</dc:subject>
          <dc:subject>first comprehensive review</dc:subject>
          <dc:subject>congenital heart disease</dc:subject>
          <dc:subject>cardiac structures segmentation</dc:subject>
          <dc:subject>cardiac ct segmentation</dc:subject>
          <dc:subject>advanced hybrid resnet</dc:subject>
          <dc:subject>2004 &amp;# 8211</dc:subject>
          <dc:subject>chd ct segmentation</dc:subject>
          <dc:subject>8 studies ).</dc:subject>
          <dc:subject>12 studies</dc:subject>
          <dc:subject>variants dominated</dc:subject>
          <dc:subject>unet architecture</dc:subject>
          <dc:subject>training approach</dc:subject>
          <dc:subject>strong baseline</dc:subject>
          <dc:subject>small datasets</dc:subject>
          <dc:subject>six databases</dc:subject>
          <dc:subject>remains underexplored</dc:subject>
          <dc:subject>remain limited</dc:subject>
          <dc:subject>rater variability</dc:subject>
          <dc:subject>model description</dc:subject>
          <dc:subject>missing data</dc:subject>
          <dc:subject>medical imaging</dc:subject>
          <dc:subject>inclusion criteria</dc:subject>
          <dc:subject>ieee xplore</dc:subject>
          <dc:subject>highlights challenges</dc:subject>
          <dc:subject>evaluation methods</dc:subject>
          <dc:subject>evaluated using</dc:subject>
          <dc:subject>emerging domain</dc:subject>
          <dc:subject>deep learning</dc:subject>
          <dc:subject>clinical evaluation</dc:subject>
          <dc:subject>clinical co</dc:subject>
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          <dc:subject>assesses quality</dc:subject>
          <dc:subject>artificial intelligence</dc:subject>
          <dc:subject>achieved utilising</dc:subject>
          <dc:subject>945 ).</dc:subject>
          <dc:subject>2025 ).</dc:subject>
          <dc:description>&lt;p&gt;Performance metrics utilised across the studies (Matthews Correlation Coefficient (MCC), Fowlkes-Mallows Index (FMI), Area Under Curve (AUC), Receiver Operating Curve (ROC), Dice Similarity Coefficient (DSC), Jaccard Index (JI), Intersection Over Union (IOU), Average Distance (AD), Average Surface Distance (ASD), Average Symmetric Surface Distance (ASSD).&lt;/p&gt;</dc:description>
          <dc:date>2026-09-29T17:40:32Z</dc:date>
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          <dc:identifier>10.1371/journal.pone.0357600.g003</dc:identifier>
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