Table 1. Summary of the included studies.
| Author and Date | Description of AI | Clinical Relevance | Technical Challenges/Study Flaws | Summary/Comments |
|---|---|---|---|---|
| Vicentini et al.[24] (2011) | FM | Clinical and functional assessment for classifying the risk of developing lymphedema and its severity. | Validation by testing in actual patients is needed. | The proposed model allows standardization of rehabilitation programs, and the assistance level required by patients at every clinical stage. |
| Moreira et al.[11] (2015) | ML | Early/preclinical identification of lymphedema In breast cancer survivors via an upper body function evaluation model. | Although more feasible than traditional methods of early lymphedema detection, the results need validation by comparing to the results of methods currently in practice. | Early identification of lymphedema would permit early intervention which can improve the long term outcomes. |
| Zanagnolo et al.[9] (2016) | Robotics | Robotic-assisted hystrectomy for cervical cancer was associated with lower risk of postoperative lymphedema than open surgery. | This is a retrospective study. A stronger evidence should be obtained from an RCT. | Robotic radical hystrectomy is safe, feasible, and is associated with improved outcomes including post-operative lymphedema. |
| Arnold et al.[12] (2017) | ML | Measuring changes in transmission of Wuchereria bancrofti before and after mass treatment by comparing IgG curves in repeated cross sectional surveys. | • Mean antibody levels do not reflect a direct epidemiologic transmission parameter. | This model could be used to evaluate the success of elimination programs by accurately estimating pathogen transmission rates. |
| • Lack of a universal reference for antibody titers makes it challenging to compare means across different studies. | ||||
| Chiang et al.[13] (2018) | ML | Monitoring and providing feedback to breast cancer patients performing postoperative lymphatic rehabilitation exercises. | Validation of the system requires comparing the model with the gold-standard methods in practice. | The proposed model is more feasible than the other motion capture systems in practice. |
| Deribe et al.[14] (2018) | ML | Estimating prevalence and geographical distribution of Podoconiosis in Cameron using ML predictive model. | • Data collection may have introduced geographical bias to the study. | Accurate estimation of prevalence guide eradication and treatment plans of endemic communicable disease. |
| • Some confounders were not accounted for as economic status and personal hygeine. | ||||
| Eneanya et al.[15] (2018) | ML | Mapping lymphatic filariasis risk area in Nigeria using ML predictive model. | - | Accurate mapping of the risk area is critical for the vector eradication campaigns. |
| Fu et al.[16] (2018) | ML | Detection of lymphedema status based on real-time symptom report. | • The study depended on self-reported lymphedema status rather medical records. | The proposed model detected lymphedema with an accuracy, sensitivity, and specificity of ?90% |
| • Expanding the spectrum of data to include lymph volume is required to train and improve the algorithm predictive ability. | ||||
| Eneanya et al.[15] (2019) | ML | Mapping the prevalence of lymphatic filariasis in Nigeria. | • Selection bias towards more accessible sampling sites. | Accurate prediction of prevalence is essential for mass treatment campaigns. |
| • The nocturnal nature of the blood test may have confounded the results. | ||||
| Kistenev et al.[18] (2019) | ML | Diagnosis and staging of lymphedema by estimating collagen disorganization using mltiphoton imaging and ML. | • The sensitivity and specificity of the test were not reported. | The proposed model diagnosed lymphedema with a 96% accuracy. |
| • The ability of detecting preclinical lymphedema was not tested. | ||||
| Agarwal et al.[23] (2019) | Robotics | Robotic-assisted surgery for endometrial cancer was associated with less incidence of postoperative lymphedema than open surgery. | • A retrospective study. Stronger evvidence should be obtained from an RCT. | Robotic-assisted surgical staging for uterine cancer and is associated with fewer short-term and long-term complications. |
| Mayfield et al.[19] (2020) | ML | Estimating prevalence of lymphatic filariasis in Samoa by using a combination of geostatistics and ML. | • The data used to train the model were not randomly sampled which may bias the predictions. | Predicting prevalence of lymphatic filariasis is critical for the mass-treatment campaigns in endemic regions. |
| Chausiaux et al.[20] (2021) | ML | Evaluation of foot volume to detect lymphedema. | • The device was tested on healthy volunteers and for validation, it should be tested on patients with lower limb edema. | The proposed model is more feasible and as accurate as the standard methods. |
| Kwarteng et al.[21] (2021) | ML, DL | Recognizing risk factors of lymphatic filariasis in Ghana. | • The proposed model did not consider important risk factors that could improve predictions. | The findings of the study are critical for vector elimination and treatment campaigns. |
| Notash et al.[22] (2021) | ML | Measurment of lymphedema arm volume. | • The study did not report sensitivity or specificity of the proposed model. | The proposed model is more feasible than standard methods. |
AI, artificial intelligence; FM, fuzzy model; ML, machine learning; DL, deep learning; RCT, randomized clinical trial.