# Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence
## 6 Applications of AMI in Heathcare
\begin{table}
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**Objectives** & **Methods** & **Dataset** & **Performance** \\ \hline Automatic classification of brain tumors [120]. & MobileNetv3 & Brain tumor dataset from Kaggle & Acc: 99.75\% \\ \hline Real-time live ultrasound segmentation [124] & Dense-PSP-UNet & CLUST challenge on liver US tracking & DSC: 0.913 \(\pm\) 0.024, IoU: 0.84, Sen: 0.929, Spe: 0.979 \\ \hline Segmentation of Bowel in CT images [125] & BowelNet & Own CT dataset & DSC: 0.764 (duodenum), 0.848 (jeumumum-lieum), 0.835 (colon), 0.774 (sigmoid), and 0.824 (rectum) \\ \hline Segmentation of teeth and dental plaque of various shape [126] & SDNet & SDPSeg & MIoU: 90.35\% (teeth), 80.08\% (plaque) \\ \hline Pixel-level semantic segmentation and classification of Alzheimer’s disease [127] & M-Net & Own sMRI dataset, ADNI, OASIS & Own dataset : Acc: 99\%, ADNI: 97.1\%, OASIS : 92.1\% \\ \hline Spine fracture segmentation [128] & MSFNet & Own MRI dataset & DSC: 90.32, IoU: 91.72 \\ \hline Automatic instance counting and segmentation of food [129]. & SibNet & own food dataset (Western, Chinese, Japanese) & MAE: 0.36, PQ: 81.68\% \\ \hline Recognition of thyroid gland and neck tissue [130] & SDNet & Own dataset containing 2D thyroid ultrasound images & Average MAP: 61.1\% \\ \hline Instance segmentation of white blood cells from whole slide images [131] & MSS-WISN & Own dataset & F1: 0.901 and Dice: 0.902 \\ \hline Automatic segmentation of Nuclei[132] & DSCA-Net & PanNuke & Average MIoU: 0.5248 \\ \hline Automatic counting, segmenting, and recognizing food instances in real-time [133]. & FoodMask & Mixed Dishes (MD), UECFoodPixComp (UEC) and FoodSeg103 (FS) & F1: 87.02\% (MD), 72.91\% (UEC), 60.81\% (FS), PQ: 66.99 (MD), 61.35(UEC), 52.38(FS) \\ \hline Automatic vessel occlusions detections on CT angiography [135] & RetinaNet & Own CTA dataset & AUROC: 0.96, Sen: 94\%, Spe: 83\% \\ \hline Automatic localization of colorectal cancer lesions in CCTA images [136] & YOLOv8 & Own CCTA dataset & F1: 0.97, mAP: 0.984, Sen: 0.83, Spe: 0.97, Acc: 0.96 \\ \hline Detection and measurement of renal cysts automatically [137] & YOLOv5 UNet & Own ultrasound dataset & Mean error : 8.49 \\ \hline Automatic detection of lung granulomas [138] & CRDNet & Own dataset & mAP: 0.316 \\ \hline Automatic identification of stenotic vessel and normal vessel and qualification of degree of stenosis [139]. & CASMatching & Own DSA dataset & mAP: 95.12\%, mae : 0.378 (reference vessel diameter), 0.221 (minimum lumen diameter), 0.49 (degree of stenosis) \\ \hline Segmentation of multimodal brain tumor for smart hospitals [149] & Used UNet-LSTM model & Brain Tumor Segmentation Challenge & DSC: 0.91 (WT), 0.82(TC), 0.80(ET) \\ \hline Assessment and classification of facial paralysis [150] & Esembled 5 SVM classifiers in parallel & Own dataset & Acc: 96.8\%, Sen: 88.9\%, Spe: 99\% \\ \hline  & Phase 1: feature reduction through LDA. & \\ Early detection of PD from the voice change of a patient [151] & Phase 2: Feature extraction through sparse Auto Encoder, Phase 3: Classification using RNN-GLSTM-ADAM. & [PERSON] of the University of Oxford in collaboration with the National Center for Voice and Speech & Acc: 95.4\%, Precision: 95.8\%, Recall: 92.1\% \\ \hline Fall detection system [152] & Ensemble model using 1D CNN and LSTM & SisFall, Kfall & Sen : 99.24\%, F1 : 98.79\% \\ \hline Prediction of heart disease in AAL [153] & Used lightweight DL based QQFFN & Heart Failure Prediction Dataset from Kaggle & ROC:0.923 \\ \hline Early diagnosis of PD [154] & Used OF-k-NN classifier model & Voice Dataset DS1 and DS2 from Kaggle & Acc: 97.95\% (DS1), 91.48\% (DS2), F1: 0.98 (DS1), 0.91 (DS2), MCC: 0.93675 (DS1) and 0.79816 (DS2) \\ \hline \end{tabular}
\end{table} 
Table 14: _Cont._