Predicting weaning failure from invasive mechanical ventilation: The promise and pitfalls of clinical prediction scores

Abstract

Prediction of weaning success from invasive mechanical ventilation remains a challenge in everyday clinical practice. Several prediction scores have been developed to guide success during spontaneous breathing trials to help with weaning decisions. These scores aim to provide a structured framework to support clinical judgment. However, their effectiveness varies across patient populations, and their predictive accuracy remains inconsistent. In this review, we aim to identify the strengths and limitations of commonly used clinical prediction tools in assessing readiness for ventilator liberation. While scores such as the Rapid Shallow Breathing Index and the Integrative Weaning Index are widely adopted, their sensitivity and specificity often fall short in complex clinical settings. Factors such as underlying disease pathophysiology, patient characteristics, and clinician subjectivity impact score performance and reliability. Moreover, disparities in validation across diverse populations limit generalizability. With growing interest in artificial intelligence (AI) and machine learning, there is potential for enhanced prediction models that integrate multidimensional data and adapt to individual patient profiles. However, current AI approaches face challenges related to interpretability, bias, and ethical implementation. This paper underscores the need for more robust, individualized, and transparent prediction systems and advocates for careful integration of emerging technologies into clinical workflows to optimize weaning success and patient outcomes.

Key Words: Mechanical ventilation; Weaning; Prediction models; Artificial intelligence; Respiratory failure

Core Tip: This paper aims to review the roles and limitations of clinical prediction scores in guiding ventilator weaning decisions. It highlights the variability in score performance, the need for population-specific validation, and explores the emerging potential of artificial intelligence-driven models to enhance accuracy, personalization, and safety in invasive mechanical ventilation liberation.

INTRODUCTION

Respiratory failure is the inability of the respiratory system (combined with the upper airway, chest wall, lungs, cardiovascular system, and neurologic system) to meet oxygenation, ventilation, or metabolic demands. When noninvasive modalities, including nasal cannula, high-flow nasal cannula, venturi mask, or positive pressure ventilation with continuous positive airway pressure (CPAP), bi-level positive airway pressure, or average volume-assured pressure support fail, patients may require invasive mechanical ventilation (IMV). While patients with endotracheal intubation on mechanical ventilation are frequently encountered in the intensive care unit (ICU), the optimal timing of removing IMV support remains a challenge, with failure rates ranging from 10%-40%[1-3]. Failure after planned extubation is associated with worsening hemodynamics, increased hospital and ICU length of stay, hospital mortality, and rates of tracheostomy[4-6]. While a conservative approach is sometimes considered to prevent extubation failure, prolonging mechanical ventilation increases the risk of tracheal/laryngeal injury, barotrauma, ventilator-associated lung injury, ventilator-associated pneumonia, diaphragmatic dysfunction, increased ICU stay, ICU delirium, and mortality[6-9]. Unless a reversible risk factor for extubation failure exists that is expected to be corrected soon, the benefits of delaying extubation are offset by the consequences of prolonged mechanical ventilation. Therefore, attempts should be made on a daily basis to safely discontinue IMV as soon as possible. However, there are no ideal prediction scores or objective measures to assess the success of extubation.

CURRENT PRACTICES IN WEANING FROM MECHANICAL VENTILATION

Weaning describes a gradual process of sequentially decreasing levels of ventilatory support to allow the respiratory system to handle spontaneous respiration. Liberation implies a rapid removal of assistance that is no longer needed. Extubation refers to the final step of removing the endotracheal tube. While the terms weaning and liberation are used interchangeably, the distinction is important as weaning can delay the extubation process while liberation protocols decrease ventilator time and complications[10]. The readiness to liberate includes subjective and objective assessments of the patient’s clinical status[11,12]. Subjective assessment includes: (1) At least partial reversal of the underlying etiology of the respiratory failure requiring IMV; (2) Adequate airway patency; (3) Good cough strength, along with requiring infrequent suctioning of the airway secretions; and (4) Adequate mental status.

Objective assessment includes: (1) Stable hemodynamics; (2) Adequate gas exchange with minimal oxygen requirements of PEEP ≤ 5-8 cm H₂O, FiO₂ ≤ 50%; and (3) Improving lung mechanics. Rapid shallow breathing index (RSBI) < 105.

While patients on IMV are on continuous analgesia along with sedative drips for patient safety, comfort, and ventilator synchrony, daily spontaneous awakening trials (SAT) by interrupting the analgesics and sedatives are recommended, as they are associated with shorter duration of mechanical ventilation and length of ICU stay[13,14]. The goal for the patient is to follow simple commands, including opening their eyes, squeezing the hand, coughing upon request, and sticking out their tongue[15]. SAT is considered successful if the patient can follow commands in the absence of pain, agitation, anxiety, cardiac arrhythmias, tachypnea, oxygen desaturation, or respiratory distress. Upon encouraging subjective and objective assessments along with successful SATs, a spontaneous breathing trial is attempted.

Spontaneous breathing trial (SBT) strategies traditionally included a T-piece trial, CPAP, or automatic tube compensation[16]. However, the current guidelines recommend the initial SBT be conducted with a pressure support ventilation (PSV) with an inspiratory pressure augmentation of 5-8 cm H₂O[17]. SBT should be at least 30 minutes and no longer than 120 minutes[18]. Successful SBT criteria include respiratory rate (RR) < 35 breaths/min, heart rate (HR) < 140/min, PaO₂ > 60 mmHg on FiO₂ < 0.4, no signs of increased work of breathing with accessory muscle use, nasal flaring, profuse diaphoresis, and agitation. Successful liberation is defined as the ability to remove IMV along with the endotracheal tube and to remain without ventilatory support for up to 48 hours post-extubation. Liberation failure is defined as an unsuccessful SBT, need for reintubation, or death within 48 hours of extubation[19].

PREDICTION SCORES TO ASSESS THE SUCCESS OR FAILURE OF VENTILATOR WEANING

Several prediction scores were designed over the years to assess the success or failure of liberation from mechanical ventilation. One of the earliest designed scales for assessing IMV weaning was the Morganroth scale, developed in 1984 (Table 1). It was based on a retrospective evaluation of 11 patients, creating an adverse factor score and a ventilator score with a total of 27 variables[20]. A score under 55 was predictive of successful weaning, demonstrating a sensitivity of 93% and a specificity of 86%. Given the limited sample size, the scale was not put to use widely. Also, the Morganroth scale has not been prospectively evaluated.

Table 1 Summary of the various prediction scores to predict weaning failure.

Scoring method	Interpretation	Uses	Limitations
1 Morganroth scale	Score of < 55 predicts successful weaning	Applied to patients requiring short-term and long-term mechanical ventilation	Limited data
2 RSBI	Score of < 105 predicts successful weaning	Easy to calculate	Can be confounded by multiple patient factors
3 CROP index	Score of > 13 mL/breath/min predicts successful weaning	Includes respiratory and ventilator parameters	Cannot be used in neuro patients
4 Gluck and Corgian score	Score of < 3 predicts successful weaning	Simple bedside measurement	Not validated in large studies
5 BWAP	Score of > 50 predicts successful weaning	Comprehensive weaning checklist	Limited data
6 Modified Burns Wean Assessment Program	Score of > 60 predicts successful weaning	Useful for long-term mechanical ventilation	Limited data
7 Persian weaning tool	Score of > 50 suggests readiness to wean	Similar to BWAP	Limited data
8 HACOR scoring	Score of > 5 predicts weaning failure	Easy bedside tool	Limited data
9 WEANS NOW	Score of 1 or more predicts weaning failure	Multiple parameters included	Complex
10 ExPreS	Score of > 59 has a high probability of extubation success	Simple tool, shown to reduce extubation failure rates	Not validated in large studies

RSBI: Rapid shallow breathing index; BWAP: Burns Wean Assessment Program.

Originally introduced by Yang and Tobin[21], the RSBI is defined as the ratio of respiratory frequency to tidal volume. It is calculated during a 1-minute spontaneous breathing trial. Patients who cannot breathe without assistance tend to breathe both rapidly and shallowly, translating to higher RSBI scores. RSBI > 105 is associated with liberation failure, but values < 105 are 97% sensitive and 64% specific for successful extubation[21]. A recent meta-analysis found a pooled sensitivity of 0.60 and a pooled specificity of 0.68[22]. While it is a simple tool that stands the test of time, there are certain concerns regarding its usage. Firstly, any condition that causes tachypnea could cause falsely elevated RSBI, causing delays in ventilator liberation. These include fever, anxiety, pain, infection, severe anemia, etc. In addition, smaller size endotracheal tubes, women, and endotracheal suctioning can also cause higher RSBI scores[23]. On the contrary, certain conditions could have falsely low RSBI. In chronic obstructive pulmonary disease patients, poor inspiratory efforts do not trigger the ventilator[24]. In CHF patients, the effect of positive pressure could improve cardiac function, causing lower RSBI[25]. In neurologic conditions leading to bradypnea, RSBI could be falsely low as well[26]. In addition to falsely high or low values, there are comments that the threshold hold value should differ between the method of liberation, i.e., PSV vs T-piece[27]. However, guidelines now recommend using PSV for liberation[17].

RSBI continues to remain the most commonly used tool despite its shortcomings. Clinicians should use it, keeping in mind the possible factors causing falsely high or falsely low values. In addition to measuring RSBI as a one-time value, variants like serial RSBI measurements and RSBI rates were also evaluated. The modified entity of RSBI is the serial RSBI. This modification was introduced because some individuals initially exhibit a stable respiratory rate and depth during the early phase of the SBT, which deteriorates as the trial progresses. This may result from limited respiratory muscle strength or a decline in lung mechanics, which are not evident at the onset of weaning. It was concluded that RSBI does not change significantly during the 90 minutes of SBT during serial measurement done at 1, 30, 60, and 90 minutes[28]. Serial RSBI during 120 minutes of SBT was unable to detect liberation failure[29]. The RSBI rate is defined as the change in RSBI over time based on serial measurements. Segal et al[30] hypothesized that the RSBI rate is a more accurate indicator of successful weaning. Their study showed a sensitivity of 90.4% and a specificity of 100%, concluding that the percentage change in RSBI during SBT was a superior predictor of successful extubation compared to RSBI.

Martin J. Tobin and Karl Yang also introduced the CROP index simultaneously when RSBI was introduced as a comparative weaning tool. The CROP index integrated four parameters, i.e., thoracic compliance, respiratory rate, arterial oxygenation, and maximum inspiratory pressure[21]. Sensitivity was highest for maximum inspiratory pressure (100%), followed by RSBI (97%). Specificity was highest for RSBI (64%) and lowest for maximum inspiratory pressure. The RSBI was the best predictor of successful weaning; maximum inspiratory pressure and the RSBI were the best predictors of failure. The CROP index required complex calculations and was not tested in neurologic patients. Since RSBI appeared to be the best predictor of successful weaning and failure, the CROP index added little to the prediction.

A few years after RSBI became a common practice, Gluck and Corgian created a scoring system in 1996 that used RSBI along with four additional parameters. They include the ratio of dead space to tidal volume, static lung compliance, airway resistance, and CO₂ pressure[31]. A score of < 3 predicted weaning success with sensitivity, specificity, and positive and negative predictive values of 1.0, 0.91, 0.83, and 1.0, respectively. While it is a simple bedside tool, the sample size was small.

In 1998, a tool by the Burns Wean Assessment Program was designed to reduce practice variability in ventilator management and readiness for weaning from the ventilator. It is a checklist of 26 clinical factors, including the patient’s pulmonary, physiological (respiration, nervous status, diet, and hemodynamic), and psychological components. A BWAP score of > 50 predicted successful weaning from the ventilator[32,33]. A modified version of BWAP (mBWAP) was used in a follow-up study in 2014 by Jiang et al[34]. A mBWAP score of > 60 predicted successful extubation with a sensitivity of 81.4% and specificity of 82.1%. While the mBWAP score has shown better predictive rates, the literature on the use of BWAP and mBWAP is still limited. A closely comparable score to the BWAP score is the Persian weaning tool designed by Irajpour et al[35] in 2014. It includes 26 parameters covering the respiratory, cardiovascular, and general clinical status of the patient. However, there is limited literature regarding its utility.

In 2017, Duan et al[36] designed a HACOR scoring with heart rate, acidosis, consciousness, oxygenation, and respiratory rate as the components. It was later updated in 2022, adding baseline data including the presence of pneumonia, cardiogenic pulmonary edema, pulmonary ARDS, immunosuppression, or septic shock, and the SOFA score[37]. It was found that the HACOR score of > 5 predicted a weaning failure sensitivity of 83.8% and a specificity of 96.4% with an area under the curve (AUC) of 0.95[38]. HACOR also reflects on cardiac dysfunction and diaphragmatic dysfunction, but, in contrast to the RSBI, calculating the HACOR score at multiple time points during an SBT is more challenging. Given the limited studies based on HACOR scores, it requires additional trials.

In 2020, the WEANS NOW score was developed by Lin et al[39]. The score was derived using eight components, including weaning parameters, endotracheal tube, arterial blood gas analysis, nutrition, secretions, neuromuscular-affecting agents, obstructive airway problems, and wakefulness. The study reported that a WEANS NOW score of ≥ 1 was associated with extubation failure. Given the complexity of the scoring, the practicality of common bedside application remains questionable.

A relatively recent score developed in 2021 by Baptistella et al[40] is the extubation predictive score (ExPreS). The score aimed at combining both respiratory and non-respiratory parameters. RSBI in SBT, dynamic lung compliance, duration of IMV, muscle strength, estimated Glasgow Coma Scale (GCS), hematocrit, and serum creatinine, along with the presence of neurologic comorbidity, were used to create the ExPreS. A score of > 59 indicated a high probability of extubation success. ExPreS score appears to be a more comprehensive assessment of the clinical status rather than limited to respiratory status. However, further large-scale studies are needed for validation.

Despite several prediction scores developed over the years, no ideal tool could confidently guide physicians in predicting liberation success. Therefore, the onus of the optimal timing of ventilator liberation still lies on the physicians’ clinical evaluation. However, additional tools like ultrasound and artificial intelligence have come into consideration over the years.

ULTRASOUND TO ASSIST IN WEANING

Ultrasound-based management has become a regular practice in the ICUs. The most common cause of extubation failure is diaphragmatic dysfunction, which worsens over time with the patient being on prolonged mechanical ventilation[41]. Therefore, ultrasound-based diaphragmatic indices could be used for liberation prediction.

Activities of the diaphragm, like diaphragmatic excursion (DE) and diaphragmatic thickening fraction (DTF), have been used extensively. It is performed using Brightness (B) and Motion (M) modes from the subcostal view. M mode captures diaphragm movement along the selected line, with three measurements taken and averaged. DTF, which is performed during quiet, spontaneous breathing, is calculated as the (thickness at the end of inspiration - thickness at the end of expiration)/thickness at the end of expiration × 100. It is measured as the distance between the pleural line and the middle of the peritoneal line. It is estimated three times on the same scan, and the average results were used. AUC with cut off value for DE being 11.43mm revealed a sensitivity of 77.8%, specificity of 84.6%, PPV 87.5%, NPV 73.3% compared to RSBI which showed a sensitivity of 66.7, specificity of 53.8%, PPV 66.7%, NPV 53.8% showing the superiority of the diaphragmatic ultrasound, especially DE[42]. Multiple meta-analyses have supported the role of DE and DTF in predicting weaning failure[43-46].

Lung ultrasound score (LUS) is another tool to quantitatively assess whether lung ventilation is compromised or adequate. LUS is a semi-quantitative bedside tool that translates sonographic lung findings into a numerical scale, allowing clinicians to track the extent and progression of pulmonary aeration loss in real time. By scanning twelve standardized thoracic zones and grading patterns such as A-lines (normal aeration), coalescent or separated B-lines (interstitial syndrome), and consolidations or pleural effusions, a composite score, from 0 (normal lungs) to 36 (severe loss of aeration), is generated[47]. Patients with LUS scores between 1 and 10 had a high rate of successful extubation, while those with scores between 16 and 32 had a higher rate of extubation failure[48]. A meta-analysis supported the utility of LUS as a valuable predictor of extubation failure, with a pooled sensitivity of 0.86, specificity of 0.75, and an AUC of 0.87, indicating high reliability[49].

WEANING AND ARTIFICIAL INTELLIGENCE

Artificial intelligence (AI) began in the 1950s with early programs, and later evolved into machine learning and neural networks in the 1980s and 1990s. By the 2000s, advancements in natural language processing and computer vision led to the development of modern intelligent tools such as virtual assistants.

AI and machine learning (ML) transform modern medicine by analyzing vast, complex datasets like medical records, images, and physiological metrics beyond human capability. For example, AI has shown to help prevent ventilator-associated pneumonia by identifying high-risk patients and improving decisions about mechanical ventilation (MV) weaning. ML models use patient parameters such as tidal volume and heart rate to make accurate predictions, reducing complications. Supervised ML handles classification and regression, while unsupervised ML focuses on clustering and dimensionality reduction. Advanced methods like deep learning and neural networks enable high-accuracy tasks like image recognition[50].

Hsieh et al[51] constructed an artificial neural network model to predict successful extubation from mechanical ventilation (Table 2). The model was trained using data from 3602 patients and included 37 clinical risk factors. The overall performance of this model had an AUC of 0.85, which was superior to traditional scoring systems and rapid shallow index testing. Techniques such as K-fold cross-validation are employed when data is limited. In this technique, the original sample is divided into K subsets. Huang et al[52] involved 233 patients, with 28 (12%) experiencing failed extubation. The study achieved an AUC value of 0.976 and an accuracy of 94% to predict extubation outcomes. The RF model provided precise real-time extubation outcome predictions for patients at different time points. An AI/ML model has the potential to offer intensive calculations and regression analysis. This provides an optimal action plan for individualized weaning to improve patient outcomes.

Table 2 Summary of the various artificial intelligence models to predict weaning failure.

Ref.	Sample size	Performance metrics	Technique used	Characteristics involved	Primary outcome
Hsieh et al[51]	3602 patients	AUC-0.85, Accuracy-94%, Precision-0.939, F1-0.867, Recall-0.822	K-fold cross-validation	Age, gender, cause of intubation, MAPs, MIP, APACHE II scores, GCS	Successful extubation from MV
Huang et al[52]	233 patients	AUC-0.97, Accuracy-94%, F1 score-95.8%, Sensitivity-87.5%, Specificity-96.7%	Logistic regression, Random Forest, and support vector machine model	Gender, APACHE II score, hospital stay duration in days, MV duration in days	Successful extubation
Lin et al[53]	Part 1: 2405; Part 2: 131	Try weaning phase: AUC-0.860, Accuracy-0.768, Sensitivity-0.788, Specificity-0.733; Extubation phase: AUC-0.923, Accuracy-0.842, Sensitivity-0.842, Specificity-0.842	Logistic regression, RF, SVM, KNN, Light GBM, MLP, XGBoost	Age, APACHE II score, TISS score, FiO2, PEEP, RR, MV, Ppeak, SpO2, HR, BP	Successful extubation, MV time, ICU LOS, Hospital LOS
Liu et al[54]	Stage 1: 5873 patients; Stage 2: 4172 patients	Stage 1: AUC-0.860, Accuracy-0.768, Sensitivity-0.788, Specificity-0.733; Stage 2: AUC-0.923, Accuracy-0.842, Sensitivity-0.842, Specificity-0.842	Logistic regression, RF, SVM, KNN, Light GBM, MLP, XGBoost	25 features in stage 1, 20 features in stage 2-common variables-APACHE II scores, TISS score, FiO2, PEEP, MV, Ppeak, SpO2, HR, BP	Weaning of MV patients
Xu et al[55]	487 patients	AUC-0.805, Accuracy-0.748, Sensitivity-0.767, Specificity-0.676, Recall-0.888	Logistic regression, RF, SVM, Light GBM, XGBoost	RR, SBT, APACHE II score, GCS, Hb	Weaning of MV patients

AUC: Area under curve; BP: Blood pressure; FiO₂: Fraction of inspired oxygen; GCS: Glasgow coma scale; HR: Heart rate; LOS: Length of stay; MAP: Mean airway pressure; MIP: Maximal inspiratory pressure; MV: Mechanical ventilation; PEEP: Positive end-expiratory pressure; Ppeak: Peak airway pressure; RR: Respiratory RATE; SPO₂: Peripheral oxygen saturation; TISS: Therapeutic intervention scoring system.

Lin et al[53] described in a study how AI improved weaning in medical ICUs and coronavirus disease 2019 (COVID-19) ICUs. The study was divided into two parts: Before AI implementation and after AI implementation. The first part compared outcomes in the medical ICU before and following AI implementation. The second part occurred during the COVID-19 pandemic, where patients were divided into control (without AI assistance) and intervention (with AI assistance) groups from August 1, 2022 to April 30, 2023.

In the first part, the intervention group demonstrated a shorter mean MV time (144.3 hours vs 158.7 hours, P = 0.077), ICU length of stay (LOS) (8.3 days vs 8.8 days, P = 0.194), and hospital LOS (22.2 days vs 25.7 days, P = 0.001) compared to the pre-intervention group (without AI, n = 1298). In the second part, the intervention group (with AI, n = 88) exhibited a shorter mean MV time (244.2 hours vs 426.0 hours, P = 0.011), ICU LOS (11.0 days vs 18.7 days, P = 0.001), and hospital LOS (23.5 days vs 40.4 days, P < 0.001).

The integration of AI into the weaning protocol led to improvements in the quality and outcomes of MV patients. AI not only aids in the successful prediction of extubation but also assists in the weaning of mechanical ventilation. The study conducted in two stages by Liu et al[54] was reviewed and accepted by the Chi Mei Medical Center (CMMC) and conducted according to approved guidelines and regulations. The try-weaning stage involves switching the ventilator from control mode to support mode for an ICU patient, while the complete weaning MV stage refers to transitioning from support mode ventilation to oxygen therapy or extubation for an ICU patient. The findings indicate that AI assistance can help predict successful extubation in ICU patients; however, further research is needed to explore its potential in predicting weaning and extubation outcomes.

Xu et al[55] compared five machine learning models: Logistic regression, random forest, support vector machine, light gradient boosting machine, and extreme gradient boosting. The evaluation was based on AUC and accuracy as performance metrics. The random forest algorithm exhibited the best performance among the five machine learning algorithms, achieving a high accuracy with an AUC of 0.805, while the extreme gradient boosting model displayed a similarly high accuracy with an AUC of 0.800. The results indicate that the random forest model demonstrates the highest performance.

However, there are certain challenges in integrating AI into ICU workflow as AI relies on high-quality and standardized data; hence, the inconsistent data entry, missing values, and variability in health records can compromise the accuracy. Apart from the challenges, the barriers for real-time application of AI are that it requires significant computational power and needs regular updates with new data to maintain accuracy.

While incorporating AI into healthcare offers exciting possibilities, it also raises a cluster of genuine concerns[56]. Firstly, it raises concerns related to patient safety and clinical effectiveness. AI models typically output probabilities and not certainties. Despite the availability of objective data, poorly calibrated scores could make wrong or inefficient predictions, causing potential weaning failures. Secondly, training of AI models would require a large volume of sensitive data, and patients may feel obliged to share data or try AI-driven therapies out of fear of wanting to stay compliant. Any breaches in technology could raise concerns related to data security and privacy[57]. In addition, there remains a lack of trust among patients and providers due to the complexity of AI systems and their inability to provide human-interpretable rationales for a recommended treatment, i.e., "Black-box" cognition. Additionally, fear of overreliance on AI, especially when data is inconclusive, contributes to hesitancy in clinical adoption. Furthermore, there can be concerns of deployment bias, i.e., using a well-trained AI model in the wrong clinical setting. Healthcare systems can consider a few ways to mitigate concerns related to AI use. They include the use of diverse, well-curated training datasets, ongoing training to maintain human expertise, multidisciplinary AI ethics boards, and, above all, still keep clinical judgment as the priority.

The integration of ultrasound parameters into AI models for mechanical ventilation weaning and extubation is a promising approach to improve decision-making in critical care. Key quantitative metrics such as diaphragm thickening fraction, diaphragmatic excursion, and lung aeration scores should be extracted in real time. These features can then be time-aligned with traditional ventilator and hemodynamic signals (e.g., tidal volume, respiratory rate, heart rate) to create models that learn temporal patterns predictive of successful spontaneous breathing trials. Incorporating clinician-annotated outcomes (extubation success or failure within 48 hours) as supervised labels enables the model to calibrate ultrasound features, while techniques such as transfer learning and domain adaptation help maintain performance across different ultrasound machines and patient populations. However, challenges such as protocol standardization, data integration, and clinical validation must be addressed for widespread adoption.

Future advancements in AI for clinical use may include user-friendly ML interfaces. These will enable easy data input and real-time recommendations via mobile apps and could also predict long-term recovery and support remote weaning in telemedicine. However, challenges remain, including validation across diverse populations, addressing ethical concerns, ensuring privacy of the data, and mitigating bias in algorithms. Addressing these challenges would require careful development and oversight to make sure that AI improves clinical decision-making and patient care outcomes.

CONCLUSION

Clinical prediction scores play a valuable role in guiding ventilator weaning decisions by providing objective criteria to assess patient readiness. While these scores support clinical judgment, they should not replace individualized assessment by experienced providers. Further research is needed to refine existing tools, explore machine learning approaches, and validate prediction models across broader populations. Incorporating point-of-care ultrasound into the ventilator-weaning process adds a rapid, bedside window into the heart, lungs, and diaphragm, allowing clinicians to move beyond “trial-and-error” extubation. By objectively confirming adequate diaphragmatic excursion, diaphragmatic thickening fraction, and lung ultrasound scoring in real time, ultrasound helps identify patients truly ready to breathe independently.

In addition to ultrasonographic evaluation, artificial intelligence-based ventilator weaning represents a promising advancement in critical care medicine. By leveraging real-time data and predictive analytics, AI systems can assist clinicians in making more accurate, timely decisions regarding when and how to wean patients from mechanical ventilation. This not only enhances patient outcomes by reducing the risks associated with prolonged ventilation but also optimizes ICU resource utilization. However, challenges such as data privacy, algorithm transparency, and the need for robust clinical validation remain. As research and technology continue to evolve, integrating AI into ventilator weaning protocols could become a cornerstone of personalized and efficient patient care. Ultimately, the goal remains the same: To safely and efficiently liberate patients from mechanical ventilation, improving outcomes while minimizing risks.