Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology 1 A clustering-based approach for prediction of cardiac resynchronization therapy (CRT; 心臟再同步治療 ) Advisor ： Dr. Hsu Reporter ： Wen-Hsiang Hu Author ： Heng Huang et al ACM Symposium on Applied Computing

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 2 Outline Motivation Objective Introduction Methods Image Acquisition 、 Surface Tracking 、 Similarity Measure 、 Hierarchical Clustering 、 Cross Correlation 、 Pacing Sites Filtering Results Conclusions Personal Opinion

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 3 Motivation  In a traditional CRT device deployment, pacing sites ( 心律部位 ) are selected without quantitative prediction. That runs the risk of suboptimal benefits.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 4 Objective  achieve maximized CRT benefit. ─ Estimate the most effective places for implanting the pacemaker ( 節律器 ).

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 5 Methods  MRI ( 核磁共振影像 ) is used to capture 3D images of a heart  Each MRI sequence holds 17 to 20 temporal phases per heartbeat.  {Phase 1 Slice 6 Radius 8, Phase 2 Slice 6 Radius 8,…, Phase 20 Slice 6 Radius 8 } represents a sample radial motion series for the 8-th radius in Slice6.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 6 Methods  Hierarchical Clustering: bottom-up clustering & average-link approach  Cross Correlation : obtain timing delay value  Pacing Sites Filtering: Using the contraction timing delay between pacing site candidates and main cluster, we filter out the site candidates without contraction delay. sweep line

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 7 Results  We implement and test our approaches on 20 patients‘ cardiac MRI data.  {93,94} => candidate site  {82,83} => candidate site  filter out {1,2,3}  move sweep-line from top to bottom till extracting two clusters sweep line

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 8 Results (cont.) abnormal heart normal heart

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 9 Results (cont.)

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 10 CONCLUSIONS  In this paper ─ identify the prior optimal left ventricle pacing sites ─ help the diagnosis of heart failure in clinic. ─ Blinded analysis of clinical MRI data also show that our approach can correctly distinguish the failing hearts from normal hearts.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 11 Personal Opinion  Disadvantage ─ unclear content  Application ─ the prediction of bi-ventricular pacing sites