However, despite the identification of risk factors and implementation of preventive strategies, rebleeding persists in clinical practice because the monitoring and diagnosis of postoperative rebleeding heavily rely on CT. For critically ill patients, routine daily CT follow-ups serve as a vital tool for monitoring, enabling early warning or confirmation of rebleeding through the identification of imaging markers, such as the “blend sign” [
176,
177]. As the diagnostic gold standard, CT provides definitive anatomical evidence. However, this monitoring is inherently point in time and cannot achieve real-time continuous surveillance. Therefore, a monitoring paradigm capable of delivering continuous, proactive bedside alerts that precede imaging changes is necessary for clinical practice. Future monitoring models for postoperative rebleeding in ICH should transition from the current “lagging and passive” approach toward a multidimensional, continuous, closed-loop early warning system characterized by “real-time, proactive, and intelligent” capabilities. To achieve this goal, studies should focus on integrating machine learning (ML) and artificial intelligence (AI) technologies. This involves consolidating multisource data from clinical, imaging, laboratory, and surgical operations to construct dynamic and individualized models for predicting rebleeding risk. By incorporating continuous data streams, such as real-time blood pressure variability and dynamic changes in coagulation function, risk levels can be updated in a real-time manner. This enables precise identification of high-risk patients and can help optimize their monitoring strategies. For example, Hall et al. [
178] developed and validated an ML model based on data from patients with ICH. This model integrated multidimensional features, including hematoma volume, medication history, demographics, and pre-ICH modified Rankin scale scores. The results showed that its area under the curve for predicting rebleeding was significantly higher than traditional scoring systems [
178]. Furthermore, ML models exhibited strong performance in predicting in-hospital mortality from ICH, with significantly enhanced predictive accuracy when combining clinical and imaging data [
179]. These studies highlight the substantial advantages of AI models in integrating complex, nonlinear clinical data, providing a viable tool for achieving precise and individualized risk stratification. Simultaneously, this strategy establishes a crucial foundation for future development of personalized models predicting the risk of rebleeding. Future studies should also develop microsensors implantable within hematoma cavities. For instance, biochemical sensors can allow continuous monitoring of hemoglobin concentration, partial pressure of oxygen, pH, or specific biomarkers (e.g., D-dimer) in the drainage fluid or interstitial fluid, acting as “chemical alarms” to detect early-stage bleeding. Integration of physical sensors, which can help simultaneously monitor ICP and Doppler blood flow signals, can enable dual validation through “physical monitoring + blood flow assessment”. Miniaturization and integration represent key trends in the ongoing advancement of sensor technology. Miniature sensors, such as one-dimensional implantable sensors, enable real-time, precise monitoring of physiological and pathological parameters with minimal invasiveness [
180]. Simultaneously, sensors integrating multiple sensing functions, such as those combining pressure, temperature, and flow rate monitoring, offer more comprehensive surveillance [
181]. The injectable, bioabsorbable, and wireless meta-structured hydrogel (metagel) sensor reported by Tang et al. [
182] represents a cutting-edge technology for ultrasound monitoring of intracranial signals. Implanted into the intracranial space with a puncture needle, the metagel deforms in response to changes in the physiological environment, causing peak frequency shifts of reflected ultrasound waves that can be wirelessly measured using an external ultrasound probe. The metagel sensor can independently detect ICP, temperature, pH, and flow rate, providing a detection depth of 10 cm and fully degrading within 18 weeks [
182]. These studies demonstrate the flexibility, biodegradability, and high precision of miniaturized sensors, which perfectly meet the need for short-term, continuous, and safe monitoring of hematoma following MIS for brain hemorrhage. They point the way toward developing next-generation in vivo postoperative monitoring systems. Innovations in bedside imaging technologies and AI-assisted diagnosis are equally critical. Contrast-enhanced ultrasound (CEUS), performed at the bedside, can indicate active bleeding by detecting contrast agent “extravasation” and holds promise as a routine postoperative screening tool. In addition, deep-learning-based algorithms should be developed to enable automatic registration, segmentation, and comparison of continuous CT images, sensitively identifying microhematoma changes even smaller than 5 ml. Automated alert mechanisms can significantly enhance diagnostic efficiency and sensitivity. In summary, an ideal real-time monitoring system for postoperative rebleeding in ICH should be AI-driven, encompassing dynamic risk prediction before and after surgery. It should guide the implantation of in-body sensors and continuous monitoring for high-risk patients, rapidly initiating CEUS verification after detecting abnormalities. This forms a complete “predict-monitor-verify” closed-loop management process. Such a system holds promise for achieving early diagnosis and management of rebleeding (Fig.
6). In the closed-loop management envisioned by this study, AI-driven dynamic prediction and in vivo sensor monitoring form a complementary, organic whole with routine CT examinations. The AI system acts as an ever-vigilant “physiological radar”, continuously identifying high-risk patients. Upon detecting escalating risks or abnormal signals, it immediately triggers targeted, advanced examinations, such as CEUS or CT. This model optimizes imaging resources from “fixed-schedule screening” to “intelligent precision validation”. It preserves the authority of CT in anatomical diagnosis while endowing the entire monitoring system with unprecedented continuity and foresight. Thus, this system can help evolve from “periodic snapshots” to “continuous surveillance + intelligent shutter”.