# Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence
## 6 Applications of AMI in Heathcare
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**Objectives** & **Methods** & **Dataset** & **Performance** \\ \hline To monitor and measure all patient and caregiver activities within an ICU bed space [11]. & YOLOv4 & ICU video from Box Hill Hospital between March 2019-May 2020 & \\ \hline Automatic classification of Autism spectrum disorder and Alzheimer’s disease [73] & LG-GNN & ABIDE, ADNI & Acc: 81.75\%, Sen: 83.22\%, F1: 82.96\% \\ \hline Automatic brain tumor segmentation [74] & Hybrid-PA-Net & BraTS 2021 and BraTS 2019 & Acc: 98.7\% [BraTs 2021], 99.3\% \\ \hline Detection of abnormal retinal fluids from the Spectral Domain OCT images [76] & FAM-U-Net & RETOUCH, OPTIMA,DUKE & DSC : 0.887 (RETOUCH), 0.786 (OPTIMA), 0.821 (DUKE) \\ \hline Automatic segmentation of teeth on panoromic radiographs [80] & PanoforTeeth & UFBA-UESC Dental Images Dataset & Acc: 97.25\%, Specificity: 97.65\%, Precision: 95.13\%, Recall: 93.92\%, F1: 93.47\%, mean average precision: 71.5\%. \\ \hline Automatic segmentation of tooth [81] & ms-SP Net & Ctooth dataset & mean IoU: 87\%, F1: 98.9\%, Acc: 98.5\%, recall: 93\%, precision: 94.5\%, DSC: 94.5\% \\ \hline Automatic segmentation of vertebral bodies in lateral cervical and lumbar spine X-ray images [83] & VertXNet & MEASUURE I, PREVENT,NHANES II & Dice: 0.90 \\ \hline RoI segmentation from ultrasound images [84] & MEF-UNet & BUSI, DDTL and BUS & IoU: 0.7221 (BUSI: benign), 0.6308 (BUSI: malignant), Dice: 0.5762 (DDTL), 0.7115 (BUSI: benign), 0.7278 (BUSI: malignant) \\ \hline Multiclass segmentation incorporating transformer-based techniques [87]. & MISFormer & Synapse, ACDC, DRIVE & DSC: 81.96\% (Synapse), 91.19\% (ACDC). Acc: 96.03\% \\ \hline Automatic detection and classification of lung cancer [88] & YOLOv6 & LUNA 16 Challenge & Acc: 82.79\% \\ \hline Automatic classification of whole slice image patches [89] & MuRCL & Camelyon16, TCGA-Lung and TCGA-Kidney & Acc: 91.32\% (Camelyon16), 89.19\% (TCGA-lung), 86.26\% (TCGA-Kidney) \\ \hline Multi-class semantic segmentation of breast cancer tissue [91] & DRD-UNet & BCSS Challenge & Acc : 0.81 \\ \hline Gland instance segmentation in histology images [92] & IAFMSMB Net & GlaS, CRAG, RINGS & Dice: 0.906 (CCRAG), 0.939 (GlaS A), 0.889 (GlaS B), 0.904 (RINGS) \\ \hline Skin lesions segmentation from dermoscopic images [93]. & Act-AttSegNet & ISIC 2017 Challenge dataset, PH2 & Acc : 0.935, Dice: 0.872, Sen: 0.897, Spe: 0.968 \\ \hline Real-time instance segmentation for ultrasound media nerve images with weak supervision [104]. & CoarseInst & UGRA and CTS & Average precision : 49.8\% \\ \hline Real-time instance segmentation of precise surgical instrument [105] & ROBUST-MIS & Multi instance dic: 0.46 \\ \hline Automatic identification of COVID19 slices from X-Rays [118] & EfficientNetB4 & Own lungs X-ray dataset & Acc: 100\% \\ \hline \hline \end{tabular}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline  & Ensemebled classification model (InceptionV3, Xception, Darknet19, Darknet53, EfficientNetB0, NASNet Mobile, Resnet50, ResNet101) \\ \hline \hline \end{tabular}
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Table 14: Contemporary literature on the applications of AI in healthcare.