# Deep Learning-Based Algorithms for Real-Time Lung Ultrasound Assisted Diagnosis
## 4 Discussion  
The implementation of software solutions using deep learning algorithms is a step towards assisted diagnosis in lung ultrasound. Both the model and the complete solution demonstrate solid performance, validated by the results shown in the previous section. Based on studies such as [34], which demonstrate the variability in physicians' opinions, and, taking into account that the proposed method works in real-time (up to 20 fps), it could be a useful tool for clinical practice, helping physicians to quickly address lung conditions and reducing the learning curve for less experienced healthcare personnel in the field. Despite the promising results, there are some limitations in this work that require further research. One of the main ones is the limited extent of the database used to train the model. Only 689 videos from 30 patients are available, which are insufficient to obtain a fully trained model capable of generalizing the entire problem across the four artifacts sought. For example, in the case of videos with consolidations, only 58 of the videos exhibit that artifact, which also explains the high values shown in Table 1. Another limitation of the dataset is that A-Lines were not consistently labeled by the physician, as they are not pathological findings. Even though A-Lines, B-Lines, and consolidations are common artifacts found in patients with pulmonary affectation of different origins, a limitation of the dataset used is that only patients with COVID-19 are present. This would impose a bias when evaluating patients with other pulmonary diseases, which should be further tested in future studies. It should also be highlighted that the images used in this study belong to a single scanner, which is likely to be a limitation, given the unique image characteristics of each equipment in terms of noise, sampling frequency, etc., which may introduce bias to the network training and limit the cross-platform applicability of the solution. Another limitation lies in the semi-automated labelling approach used for consolidations. While the automatic labelling of pleura, B-lines, and A-lines is supported by a validated algorithm from a previous study [24], the labelling of consolidations required manual intervention guided by physician annotations. This introduces some variability to the dataset, as the identification of consolidations is inherently subjective and dependent on the physician's experience. Although this method facilitated the creation of a clinically meaningful dataset, it represents a limitation in terms of ensuring complete consistency across all annotations and highlights the need for improved automated labelling approaches in future work. Additionally, the implemented model shows an inability to always segment the complete B-line. In some cases, only part of the B-line is segmented, as is shown in Figure 17. This behaviour is corrected by the post-processing algorithm, which marks the whole scan line as affected by B-line regardless the vertical extension of the output mask. Therefore, the results shown in Tables 5 and 6 are not affected by this phenomenon, but it indicates  
Figure 16: Application visualization sample: (**a**) B-lines (orange) and consolidation (red) detection; (**b**) normal lung with A-lines (green); (**c**) probe movement detected; (**d**) B line (orange) and A-line (green) deteccon on a Lung phantom. On the right of each image the C-scan image is shown. that there is already some improvement margin in the network definition and/or in the training process. For future work, these limitations could be addressed by including new data into the training or applying transfer learning technics to adapt the result to other scanners to get a more scalable solution. New modifications to the network architecture could be also studied to improve the behaviour of the model, as well as implementing in FPGAs using proper engines adapted for that hardware architecture [35]. This can enhance its adaptability to broader clinical scenarios [36].