## 4 Problem Space for DL Applications
### Device Deployment with DL
#### 4.2.3 DL Applied in Hospital Devices  
Using DL in hospitals provides transformative potential to enhance patient care, improve operational efficiency, and advance medical research. DL with the IoMT has been applied in the following aspects. * Smart bed: A smart bed is a piece of medical equipment prominently applied in intensive care for monitoring bed-bound patients as well as collecting data for diagnosis [159] and predictions [94]. A CNN [158] is integrated with a long short-term memory model to enhance performance relative to previous models to prevent terminal patients developing pressure ulcers. Research has shown the application of CNNs [95; 160] in processing sensor data for forecasting patient outcomes and preventing bedsores. * Robotic surgery systems: Robotic surgery systems, like the Da Vinci Surgical System, use DL for improved precision and control. CNNs analyze visual data from surgical cameras, aiding surgeons with tissue recognition and instrument tracking. RNNs help predict surgical tool movement for safer operations. DL has made significant strides in autonomous systems and robotics, with applications ranging from self-driving cars to robotic process automation. In robotics, DL [7] has enhanced object recognition, manipulation, and navigation capabilities, allowing robots to perform tasks with greater precision and adaptability [177]. We comprehensively reviewed medical devices equipped with DL technologies, as summarized in Table 7. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**DeviceSensors** & **Use of Device** & **Benefits** & **Drawbacks** & **DL Integration** & **DL Model** \\ \hline MicroOptical\({}^{\text{tr}}\), Westwood, MA, USA [178] & Orthopaedic fluoroscopy & Image quality is good, low radiation exposure & Lag time, difficulties in focusing & DL algorithms can reduce lag time, enhance image focus, and improve real-time visualization in fluoroscopy procedures. & CNNs, GANs, etc. \\ \hline Opti-Wt HVDD, Wayzata, MN, USA [179] & Lapareoscopy HMD vs. video monitor & Smooth motion & Imbalance weight, single 898 ge operation & mL-powered motion tracking can improve stability, while real-time image enhancement can improve surgical precision. & CNNs, RNNs, GANs, etc. \\ \hline ANC CMOS and Google GLASS, CA, USA [180] & Image-assisted cancer resection surgery & Lightweight, wearable, less interference with surgery & Costly, lag time, bad mobility, need wireless data transport & DL-based image segmentation can improve tumor detection and surgical accuracy. & CNNs, GANs, etc. \\ \hline Richard Wolf GmbH, Knittingen, Germany [181] & Trans-anal endoscopic microsurgery & Comfortable with peripheral view, easy to use & Poor image quality & DL models can enhance image resolution and clarify for better surgical outcomes. & CNNs, GANs, etc. \\ \hline Nomad ND2000\({}^{\text{th}}\), Suford TD, USA [182] & Displays vital signs monitoring for general anesthesia & Quick response & Good for general condition & DL algorithms can predict anomalies in vital signs and automate alerts for abnormal conditions. & CNNs, RNNs, GANs, etc. \\ \hline HMZ-T2 and Wrap1200, Sony Corporation, Tokyo, Japan [185] & Sonography to educate patient & Compact, clear disease visualization, high imaging quality & Lag time, power limitation & DL can reduce lag time and enhanceography image interpretation for better education and diagnostics. & CNNs, GANs, etc. \\ \hline Google GLASS, CA, USA [184] & Transformation forensic medicine & Easy use, wearable & Bad-quality images & DL models can enhance image quality and automate forensic analysis for efficiency. & CNNs, GANs, etc. \\ \hline Primesense 1080/Xtion, Tel Aviv, Israel [185] & Assistive navigation for visually impaired patients & Easy use, provides depth and distance & No studies conducted & DL-powered depth estimation and object detection can improve navigation accuracy for visually impaired patients. & YOLO, R-CNNs, etc. \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Overview of medical devices in IoMT with DL models [2].