# Digital twins: The New Frontier for Personalized Medicine?
## 5 Surgical Planning  
DHT enables the creation of accurate, three-dimensional models of a patient's anatomy based on medical imaging data, such as CT scans or MRI sequences. These virtual replicas provide surgeons with a comprehensive and detailed visualization of the patient's anatomy, allowing for better lesion localization and a deeper understanding of complex structures, spatial relationships, and potential anatomical variations [2; 27]. Creating virtual replicas of DHT-embedded organs and systems using imaging data and other pertinent data sources allows physicians to plan and simulate surgical procedures before they are performed on real patients. DHTs enable in-depth assessment of the patient's anatomy and the morphological characteristics of the lesion, the lesion's relationships with blood vessels, and other criticalstructures, helping to determine optimal incision sites and guiding surgical resection in real time, improving patient safety and surgical outcomes [28]. Furthermore, surgeons can virtually experiment with different approaches and evaluate and compare the feasibility and results of different techniques, anticipating potential challenges or complications. For example, [PERSON] et al. demonstrated that by using estimated hepatic flow velocity as input data, a DHT model could correctly predict postoperative portal hypertension [29]. DHTs can also be integrated with virtual or augmented reality technologies to provide surgeons with an immersive and interactive planning environment, allowing for a more intuitive understanding of the patient's anatomy and facilitating better surgical planning and decision making [17]. In brain tumor surgery, the development of DHTs that faithfully replicate the morpho-functional organization of cortico-subcortical circuits will assist neurosurgeons in planning the operation, delimiting the extent of the lesion and its relationships with the cerebral connectome, thus allowing precision brain surgery as radical as reasonably acceptable to preserve the quality of life [30]. The imaging and functional data acquired in a preliminary phase will be used for the development of models of neuronavigation in the operating room to guide access and the first phases of the intervention. During the procedure, DHTs can serve as a reference tool for surgeons: by overlaying the virtual model on real-time imaging data from the operating room, surgeons can navigate and visualize patient anatomy with greater accuracy, in order to identify critical structures, avoid complications, and achieve more radical excision. Furthermore, DHT can be enriched in real-time with information collected in the operating room, for example through direct electrostimulation techniques, which allow real-time mapping of the actual connectivity in the patient's brain. The development of DHTs may also play a significant role in organ transplant procedures by improving pre-transplant planning, surgical outcomes, and post-transplant monitoring [18]. Leveraging machine learning and deep learning algorithms, DHTs can provide a prediction of transplant progress from historical data of thousands of similar cases and combine typical tendencies with the specific characteristics of the candidate patient to predict the likelihood of post-transplant complications, organ rejection, or organ failure. DHTs can be created based on anatomical data from the recipient, including medical images such as CT or MRI scans. These virtual replicas allow surgeons to visualize the patient's organs, blood vessels, and other relevant structures in a three-dimensional model: this helps to evaluate the feasibility of the transplant, identify potential challenges, and plan the surgical procedure. DHTs can help match donors with potential recipients by analyzing various factors such as blood type, tissue compatibility, and other specific patient characteristics. In the post-transplant phase, DHTs can be continuously updated with data from the transplanted organ to monitor its function and detect any anomalies.