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Integrated information modeling-based cloud-connected ultrasound diagnostic systems Cover

Integrated information modeling-based cloud-connected ultrasound diagnostic systems

Open Access
|Jun 2025

Figures & Tables

Figure 1.

Block diagram of the proposed diagnostic pipeline for cloud-connected ultrasound systems using real pulse-echo signal data.

Figure 2.

(A) Sample ultrasonic pulse-echo waveforms exhibiting typical transient–decay patterns. (B) Power consumption before and after adaptive routing, showing an average gain of 18% in efficiency. (C) Overlay of original and distorted waveforms demonstrating the impact of cloud transmission artifacts including delay, dropout, and noise.

Figure 3.

(A) Pairwise scatter plot matrix showing the distribution and interaction of diagnostic and transmission-related features across four anomaly types. Features include both waveform-derived parameters (e.g., MaxAmp, StdAmp) and device metrics (e.g., SignalDelay_ms, DeviceTemp_C, PacketLossRate). (B) Normalized distribution of OSD duration, silent gaps, and noise level, highlighting statistically significant differences between anomaly classes, supporting reliable classification by the diagnostic model.

Figure 4.

Bar chart comparing anomaly detection results from Isolation Forest, One-Class SVM, and Autoencoder models. Normal vs. anomalous classification counts are shown per model, highlighting differences in sensitivity and selectivity across methods.

Multiclass Classification Performance of the Diagnostic System

Anomaly TypePrecisionRecallF1-ScoreSupport
Normal1.001.001.00273
Signal Loss1.001.001.0062
Delay1.001.001.0012
Overheating0.941.000.9716
Overall Accuracy 1.00363
Macro Avg0.991.000.99363
Weighted Avg1.001.001.00363
Language: English
Page range: 16 - 22
Submitted on: Feb 11, 2025
Accepted on: Apr 18, 2025
Published on: Jun 15, 2025
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year

© 2025 Ahmed H. Ahmed Albegli, published by Future Sciences For Digital Publishing
This work is licensed under the Creative Commons Attribution 4.0 License.