## 4 Problem Space for DL Applications
### Application Scenarios with DL
#### 4.3.5 Application Performance
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**DL Model** & **Application in IoMT** & \begin{tabular}{c} **Traditional Technology** \\ **Performance** \\ \end{tabular} & \begin{tabular}{c} **Enhanced Performance** \\ **with DL** \\ \end{tabular} \\ \hline Convolutional Neural Networks (CNNs) [93; 94; 95] & \begin{tabular}{c} Image analysis (e.g., \\ classification, segmentation, \\ detection) \\ \end{tabular} & 70\% accuracy, manual feature extraction [203] & Accuracy: COVID-19 CT image detection: 97.0\% with VGG19 algorithm [202] \\ \hline Deep Neural Network (DNN) Transformer & \begin{tabular}{c} Multimodal input fusion \\ (e.g., imaging data, \\ clinical data) \\ \end{tabular} & With machine learning (ML) models [204] & \begin{tabular}{c} Grows in accuracy by 15\% and \\ reduces in time complexity by \\ 32\% [204] \\ \end{tabular} \\ \hline Recurrent Neural Networks (RNNs) [96; 97] & \begin{tabular}{c} Time-series analysis (e.g., \\ vital signs, ECG, EEG data) \\ \end{tabular} & \begin{tabular}{c} High false alarm rate, difficulty \\ detecting complex patterns \\ \end{tabular} & \begin{tabular}{c} 90\%+ accuracy in anomaly \\ detection, real-time alerts [205] \\ \end{tabular} \\ \hline Long-Short Term Memory (LSTM) [98; 99] & \
\begin{tabular}{c} Time-series data analysis \\ (e.g., ECG, EEG, blood \\ pressure monitoring) \\ \end{tabular} &
\begin{tabular}{c} Accuracy of 76.6\% with \\ Wind-Driven Optimization \\ (WDO) [206] \\ \end{tabular} & Accuracy of 97.6\% [206] \\ \hline \hline \end{tabular}
\end{table} 
Table 10: DL performance. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Case Study** & **Real-Life Example** & **Remarks** \\ \hline Remote patient monitoring for chronic diseases & Apple Watch detecting atrial fibrillation using ECG data. & Robust preprocessing, battery efficiency, and user adoption are critical. \\ \hline Predictive analytics in smart hospitals & Mount Sinai Hospital’s DL-powered system & Interoperability, low-latency networks, and diverse training data improve results. \\ \hline Medical imaging and IoMT & \begin{tabular}{c} CoviDetect X-ray scanners aiding rapid \\ Integration \\ \end{tabular} & \begin{tabular}{c} Data augmentation and cloud integration \\ enable rapid, accurate diagnostics. \\ \end{tabular} \\ \hline Personalized medicine & \
\begin{tabular}{c} Medtronic’s AI-powered insulin pumps for \\ diabetes management. \\ \end{tabular} &
\begin{tabular}{c} Privacy-preserving techniques and adaptability \\ enhance personalized care. \\ \end{tabular} \\ \hline Smart beds for patient monitoring & IoMT-enabled smart beds reducing fall injuries & Reliable sensors and edge AI improve real-time monitoring accuracy. \\ \hline Early detection of neurological disorders & DL models analyzing EEG and sensor data for early Parkinson’s diagnosis. & Feature engineering and longitudinal data \\ \hline Assistive technologies for elderly and disabled patients & Ossur’s AI-powered prosthetics adapting to & Human-centric design and robust continuous \\ \hline \hline \end{tabular}
\end{table} 
Table 9: DL in IoMT: Case studies and best practice.