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1.
Sensors (Basel) ; 21(24)2021 Dec 13.
Artigo em Inglês | MEDLINE | ID: mdl-34960425

RESUMO

Cleaning is one of the fundamental tasks with prime importance given in our day-to-day life. Moreover, the importance of cleaning drives the research efforts towards bringing leading edge technologies, including robotics, into the cleaning domain. However, an effective method to assess the quality of cleaning is an equally important research problem to be addressed. The primary footstep towards addressing the fundamental question of "How clean is clean" is addressed using an autonomous cleaning-auditing robot that audits the cleanliness of a given area. This research work focuses on a novel reinforcement learning-based experience-driven dirt exploration strategy for a cleaning-auditing robot. The proposed approach uses proximal policy approximation (PPO) based on-policy learning method to generate waypoints and sampling decisions to explore the probable dirt accumulation regions in a given area. The policy network is trained in multiple environments with simulated dirt patterns. Experiment trials have been conducted to validate the trained policy in both simulated and real-world environments using an in-house developed cleaning audit robot called BELUGA.


Assuntos
Robótica
2.
Sci Rep ; 11(1): 22378, 2021 Nov 17.
Artigo em Inglês | MEDLINE | ID: mdl-34789747

RESUMO

Drain blockage is a crucial problem in the urban environment. It heavily affects the ecosystem and human health. Hence, routine drain inspection is essential for urban environment. Manual drain inspection is a tedious task and prone to accidents and water-borne diseases. This work presents a drain inspection framework using convolutional neural network (CNN) based object detection algorithm and in house developed reconfigurable teleoperated robot called 'Raptor'. The CNN based object detection model was trained using a transfer learning scheme with our custom drain-blocking objects data-set. The efficiency of the trained CNN algorithm and drain inspection robot Raptor was evaluated through various real-time drain inspection field trial. The experimental results indicate that our trained object detection algorithm has detect and classified the drain blocking objects with 91.42% accuracy for both offline and online test images and is able to process 18 frames per second (FPS). Further, the maneuverability of the robot was evaluated from various open and closed drain environment. The field trial results ensure that the robot maneuverability was stable, and its mapping and localization is also accurate in a complex drain environment.

3.
Sensors (Basel) ; 21(21)2021 Nov 01.
Artigo em Inglês | MEDLINE | ID: mdl-34770593

RESUMO

Human visual inspection of drains is laborious, time-consuming, and prone to accidents. This work presents an AI-enabled robot-assisted remote drain inspection and mapping framework using our in-house developed reconfigurable robot Raptor. The four-layer IoRT serves as a bridge between the users and the robots, through which seamless information sharing takes place. The Faster RCNN ResNet50, Faster RCNN ResNet101, and Faster RCNN Inception-ResNet-v2 deep learning frameworks were trained using a transfer learning scheme with six typical concrete defect classes and deployed in an IoRT framework remote defect detection task. The efficiency of the trained CNN algorithm and drain inspection robot Raptor was evaluated through various real-time drain inspection field trials using the SLAM technique. The experimental results indicate that robot's maneuverability was stable, and its mapping and localization were also accurate in different drain types. Finally, for effective drain maintenance, the SLAM-based defect map was generated by fusing defect detection results in the lidar-SLAM map.


Assuntos
Aves Predatórias , Robótica , Algoritmos , Animais , Humanos
4.
Sensors (Basel) ; 21(18)2021 Sep 18.
Artigo em Inglês | MEDLINE | ID: mdl-34577486

RESUMO

Staircase cleaning is a crucial and time-consuming task for maintenance of multistory apartments and commercial buildings. There are many commercially available autonomous cleaning robots in the market for building maintenance, but few of them are designed for staircase cleaning. A key challenge for automating staircase cleaning robots involves the design of Environmental Perception Systems (EPS), which assist the robot in determining and navigating staircases. This system also recognizes obstacles and debris for safe navigation and efficient cleaning while climbing the staircase. This work proposes an operational framework leveraging the vision based EPS for the modular re-configurable maintenance robot, called sTetro. The proposed system uses an SSD MobileNet real-time object detection model to recognize staircases, obstacles and debris. Furthermore, the model filters out false detection of staircases by fusion of depth information through the use of a MobileNet and SVM. The system uses a contour detection algorithm to localize the first step of the staircase and depth clustering scheme for obstacle and debris localization. The framework has been deployed on the sTetro robot using the Jetson Nano hardware from NVIDIA and tested with multistory staircases. The experimental results show that the entire framework takes an average of 310 ms to run and achieves an accuracy of 94.32% for staircase recognition tasks and 93.81% accuracy for obstacle and debris detection tasks during real operation of the robot.


Assuntos
Aprendizado Profundo , Percepção de Forma , Robótica , Algoritmos
5.
Sensors (Basel) ; 21(16)2021 Aug 06.
Artigo em Inglês | MEDLINE | ID: mdl-34450767

RESUMO

Routine rodent inspection is essential to curbing rat-borne diseases and infrastructure damages within the built environment. Rodents find false ceilings to be a perfect spot to seek shelter and construct their habitats. However, a manual false ceiling inspection for rodents is laborious and risky. This work presents an AI-enabled IoRT framework for rodent activity monitoring inside a false ceiling using an in-house developed robot called "Falcon". The IoRT serves as a bridge between the users and the robots, through which seamless information sharing takes place. The shared images by the robots are inspected through a Faster RCNN ResNet 101 object detection algorithm, which is used to automatically detect the signs of rodent inside a false ceiling. The efficiency of the rodent activity detection algorithm was tested in a real-world false ceiling environment, and detection accuracy was evaluated with the standard performance metrics. The experimental results indicate that the algorithm detects rodent signs and 3D-printed rodents with a good confidence level.


Assuntos
Redes Neurais de Computação , Roedores , Algoritmos , Animais , Ratos
6.
Sensors (Basel) ; 21(13)2021 Jun 24.
Artigo em Inglês | MEDLINE | ID: mdl-34202746

RESUMO

Cleaning is an important factor in most aspects of our day-to-day life. This research work brings a solution to the fundamental question of "How clean is clean" by introducing a novel framework for auditing the cleanliness of built infrastructure using mobile robots. The proposed system presents a strategy for assessing the quality of cleaning in a given area and a novel exploration strategy that facilitates the auditing in a given location by a mobile robot. An audit sensor that works by the "touch and inspect" analogy that assigns an audit score corresponds to its area of inspection has been developed. A vision-based dirt-probability-driven exploration is proposed to empower a mobile robot with an audit sensor on-board to perform auditing tasks effectively. The quality of cleaning is quantified using a dirt density map representing location-wise audit scores, dirt distribution pattern obtained by kernel density estimation, and cleaning benchmark score representing the extent of cleanliness. The framework is realized in an in-house developed audit robot to perform the cleaning audit in indoor and semi-outdoor environments. The proposed method is validated by experiment trials to estimate the cleanliness in five different locations using the developed audit sensor and dirt-probability-driven exploration.


Assuntos
Robótica
7.
Sensors (Basel) ; 21(5)2021 Mar 03.
Artigo em Inglês | MEDLINE | ID: mdl-33802434

RESUMO

Regular washing of public pavements is necessary to ensure that the public environment is sanitary for social activities. This is a challenge for autonomous cleaning robots, as they must adapt to the environment with varying pavement widths while avoiding pedestrians. A self-reconfigurable pavement sweeping robot, named Panthera, has the mechanisms to perform reconfiguration in width to enable smooth cleaning operations, and it changes its behavior based on environment dynamics of moving pedestrians and changing pavement widths. Reconfiguration in the robot's width is possible, due to the scissor mechanism at the core of the robot's body, which is driven by a lead screw motor. Panthera will perform locomotion and reconfiguration based on perception sensors feedback control proposed while using an Red Green Blue-D (RGB-D) camera. The proposed control scheme involves publishing robot kinematic parameters for reconfiguration during locomotion. Experiments were conducted in outdoor pavements to demonstrate the autonomous reconfiguration during locomotion to avoid pedestrians while complying with varying pavements widths in a real-world scenario.


Assuntos
Pedestres , Robótica , Retroalimentação , Humanos , Locomoção , Percepção
8.
Sensors (Basel) ; 21(8)2021 Apr 07.
Artigo em Inglês | MEDLINE | ID: mdl-33917223

RESUMO

The pavement inspection task, which mainly includes crack and garbage detection, is essential and carried out frequently. The human-based or dedicated system approach for inspection can be easily carried out by integrating with the pavement sweeping machines. This work proposes a deep learning-based pavement inspection framework for self-reconfigurable robot named Panthera. Semantic segmentation framework SegNet was adopted to segment the pavement region from other objects. Deep Convolutional Neural Network (DCNN) based object detection is used to detect and localize pavement defects and garbage. Furthermore, Mobile Mapping System (MMS) was adopted for the geotagging of the defects. The proposed system was implemented and tested with the Panthera robot having NVIDIA GPU cards. The experimental results showed that the proposed technique identifies the pavement defects and litters or garbage detection with high accuracy. The experimental results on the crack and garbage detection are presented. It is found that the proposed technique is suitable for deployment in real-time for garbage detection and, eventually, sweeping or cleaning tasks.

9.
Sensors (Basel) ; 22(1)2021 Dec 21.
Artigo em Inglês | MEDLINE | ID: mdl-35009556

RESUMO

Vibration is an indicator of performance degradation or operational safety issues of mobile cleaning robots. Therefore, predicting the source of vibration at an early stage will help to avoid functional losses and hazardous operational environments. This work presents an artificial intelligence (AI)-enabled predictive maintenance framework for mobile cleaning robots to identify performance degradation and operational safety issues through vibration signals. A four-layer 1D CNN framework was developed and trained with a vibration signals dataset generated from the in-house developed autonomous steam mopping robot 'Snail' with different health conditions and hazardous operational environments. The vibration signals were collected using an IMU sensor and categorized into five classes: normal operational vibration, hazardous terrain induced vibration, collision-induced vibration, loose assembly induced vibration, and structure imbalanced vibration signals. The performance of the trained predictive maintenance framework was evaluated with various real-time field trials with statistical measurement metrics. The experiment results indicate that our proposed predictive maintenance framework has accurately predicted the performance degradation and operational safety issues by analyzing the vibration signal patterns raised from the cleaning robot on different test scenarios. Finally, a predictive maintenance map was generated by fusing the vibration signal class on the cartographer SLAM algorithm-generated 2D environment map.


Assuntos
Inteligência Artificial , Robótica , Algoritmos , Vibração
10.
Sensors (Basel) ; 22(1)2021 Dec 30.
Artigo em Inglês | MEDLINE | ID: mdl-35009802

RESUMO

Periodic inspection of false ceilings is mandatory to ensure building and human safety. Generally, false ceiling inspection includes identifying structural defects, degradation in Heating, Ventilation, and Air Conditioning (HVAC) systems, electrical wire damage, and pest infestation. Human-assisted false ceiling inspection is a laborious and risky task. This work presents a false ceiling deterioration detection and mapping framework using a deep-neural-network-based object detection algorithm and the teleoperated 'Falcon' robot. The object detection algorithm was trained with our custom false ceiling deterioration image dataset composed of four classes: structural defects (spalling, cracks, pitted surfaces, and water damage), degradation in HVAC systems (corrosion, molding, and pipe damage), electrical damage (frayed wires), and infestation (termites and rodents). The efficiency of the trained CNN algorithm and deterioration mapping was evaluated through various experiments and real-time field trials. The experimental results indicate that the deterioration detection and mapping results were accurate in a real false-ceiling environment and achieved an 89.53% detection accuracy.


Assuntos
Aprendizado Profundo , Robótica , Algoritmos , Animais , Redes Neurais de Computação , Roedores
11.
Sensors (Basel) ; 20(18)2020 Sep 15.
Artigo em Inglês | MEDLINE | ID: mdl-32942750

RESUMO

Insect detection and control at an early stage are essential to the built environment (human-made physical spaces such as homes, hotels, camps, hospitals, parks, pavement, food industries, etc.) and agriculture fields. Currently, such insect control measures are manual, tedious, unsafe, and time-consuming labor dependent tasks. With the recent advancements in Artificial Intelligence (AI) and the Internet of things (IoT), several maintenance tasks can be automated, which significantly improves productivity and safety. This work proposes a real-time remote insect trap monitoring system and insect detection method using IoT and Deep Learning (DL) frameworks. The remote trap monitoring system framework is constructed using IoT and the Faster RCNN (Region-based Convolutional Neural Networks) Residual neural Networks 50 (ResNet50) unified object detection framework. The Faster RCNN ResNet 50 object detection framework was trained with built environment insects and farm field insect images and deployed in IoT. The proposed system was tested in real-time using four-layer IoT with built environment insects image captured through sticky trap sheets. Further, farm field insects were tested through a separate insect image database. The experimental results proved that the proposed system could automatically identify the built environment insects and farm field insects with an average of 94% accuracy.


Assuntos
Aprendizado Profundo , Insetos , Internet das Coisas , Controle de Pragas , Animais , Redes Neurais de Computação
12.
Sensors (Basel) ; 20(16)2020 Aug 09.
Artigo em Inglês | MEDLINE | ID: mdl-32784888

RESUMO

Infectious diseases are caused by pathogenic microorganisms, whose transmission can lead to global pandemics like COVID-19. Contact with contaminated surfaces or objects is one of the major channels of spreading infectious diseases among the community. Therefore, the typical contaminable surfaces, such as walls and handrails, should often be cleaned using disinfectants. Nevertheless, safety and efficiency are the major concerns of the utilization of human labor in this process. Thereby, attention has drifted toward developing robotic solutions for the disinfection of contaminable surfaces. A robot intended for disinfecting walls should be capable of following the wall concerned, while maintaining a given distance, to be effective. The ability to operate in an unknown environment while coping with uncertainties is crucial for a wall disinfection robot intended for deployment in public spaces. Therefore, this paper contributes to the state-of-the-art by proposing a novel method of establishing the wall-following behavior for a wall disinfection robot using fuzzy logic. A non-singleton Type 1 Fuzzy Logic System (T1-FLS) and a non-singleton Interval Type 2 Fuzzy Logic System (IT2-FLS) are developed in this regard. The wall-following behavior of the two fuzzy systems was evaluated through simulations by considering heterogeneous wall arrangements. The simulation results validate the real-world applicability of the proposed FLSs for establishing the wall-following behavior for a wall disinfection robot. Furthermore, the statistical outcomes show that the IT2-FLS has significantly superior performance than the T1-FLS in this application.

13.
Sensors (Basel) ; 20(12)2020 Jun 23.
Artigo em Inglês | MEDLINE | ID: mdl-32585864

RESUMO

The role of mobile robots for cleaning and sanitation purposes is increasing worldwide. Disinfection and hygiene are two integral parts of any safe indoor environment, and these factors become more critical in COVID-19-like pandemic situations. Door handles are highly sensitive contact points that are prone to be contamination. Automation of the door-handle cleaning task is not only important for ensuring safety, but also to improve efficiency. This work proposes an AI-enabled framework for automating cleaning tasks through a Human Support Robot (HSR). The overall cleaning process involves mobile base motion, door-handle detection, and control of the HSR manipulator for the completion of the cleaning tasks. The detection part exploits a deep-learning technique to classify the image space, and provides a set of coordinates for the robot. The cooperative control between the spraying and wiping is developed in the Robotic Operating System. The control module uses the information obtained from the detection module to generate a task/operational space for the robot, along with evaluating the desired position to actuate the manipulators. The complete strategy is validated through numerical simulations, and experiments on a Toyota HSR platform.


Assuntos
Betacoronavirus , Infecções por Coronavirus/prevenção & controle , Desinfecção/instrumentação , Pandemias/prevenção & controle , Pneumonia Viral/prevenção & controle , Robótica/instrumentação , Algoritmos , COVID-19 , Infecções por Coronavirus/transmissão , Infecções por Coronavirus/virologia , Aprendizado Profundo , Desinfecção/métodos , Desenho de Equipamento , Humanos , Manutenção , Movimento (Física) , Pneumonia Viral/transmissão , Pneumonia Viral/virologia , Robótica/métodos , Robótica/estatística & dados numéricos , SARS-CoV-2
14.
Sensors (Basel) ; 20(11)2020 Jun 10.
Artigo em Inglês | MEDLINE | ID: mdl-32531960

RESUMO

Periodic cleaning of all frequently touched social areas such as walls, doors, locks, handles, windows has become the first line of defense against all infectious diseases. Among those, cleaning of large wall areas manually is always tedious, time-consuming, and astounding task. Although numerous cleaning companies are interested in deploying robotic cleaning solutions, they are mostly not addressing wall cleaning. To this end, we are proposing a new vision-based wall following framework that acts as an add-on for any professional robotic platform to perform wall cleaning. The proposed framework uses Deep Learning (DL) framework to visually detect, classify, and segment the wall/floor surface and instructs the robot to wall follow to execute the cleaning task. Also, we summarized the system architecture of Toyota Human Support Robot (HSR), which has been used as our testing platform. We evaluated the performance of the proposed framework on HSR robot under various defined scenarios. Our experimental results indicate that the proposed framework could successfully classify and segment the wall/floor surface and also detect the obstacle on wall and floor with high detection accuracy and demonstrates a robust behavior of wall following.

15.
Sensors (Basel) ; 20(6)2020 Mar 18.
Artigo em Inglês | MEDLINE | ID: mdl-32197483

RESUMO

This work presents a table cleaning and inspection method using a Human Support Robot (HSR) which can operate in a typical food court setting. The HSR is able to perform a cleanliness inspection and also clean the food litter on the table by implementing a deep learning technique and planner framework. A lightweight Deep Convolutional Neural Network (DCNN) has been proposed to recognize the food litter on top of the table. In addition, the planner framework was proposed to HSR for accomplishing the table cleaning task which generates the cleaning path according to the detection of food litter and then the cleaning action is carried out. The effectiveness of the food litter detection module is verified with the cleanliness inspection task using Toyota HSR, and its detection results are verified with standard quality metrics. The experimental results show that the food litter detection module achieves an average of 96 % detection accuracy, which is more suitable for deploying the HSR robots for performing the cleanliness inspection and also helps to select the different cleaning modes. Further, the planner part has been tested through the table cleaning tasks. The experimental results show that the planner generated the cleaning path in real time and its generated path is optimal which reduces the cleaning time by grouping based cleaning action for removing the food litters from the table.


Assuntos
Algoritmos , Aprendizado Profundo , Redes Neurais de Computação , Robótica/instrumentação , Saneamento/instrumentação , Alimentos , Humanos , Processamento de Imagem Assistida por Computador , Decoração de Interiores e Mobiliário/instrumentação , Limite de Detecção , Robótica/métodos , Equipamentos de Autoajuda , Carga de Trabalho
16.
J Clin Diagn Res ; 8(5): ZC06-8, 2014 May.
Artigo em Inglês | MEDLINE | ID: mdl-24995234

RESUMO

PURPOSE: Tranexamic acid (TXA) is prescribed for short term management of haemorrhage. It is also administered prophylatically in surgeries where blood loss is anticipated. Tranexamic mouth washes are also used in oral surgical procedures for patients with coagulopathies. The purpose of this study was to assess the efficiency of the usage of tranexamic acid on reduction of haemorrhage in maxillo mandibular trauma cases. MATERIALS AND METHODS: Twelve consecutive male patients, between the ages 20-40 years, with multiple fractures of the facial bones, were included in this study. Six patients were administered either IV tranexamic acid (10 mg/kg- Group 1)and another six placebo (IV normal saline- Group 2) just before induction of anaesthesia. All patients were operated by the same surgical team, using the same standard techniques and the same anaesthetist and the same drugs were used during the surgery. Hypotension was induced for further reduction of intra operative blood loss. Intra and post-operative blood loss, operation time, transfusion of blood products, pre- and post-operative haemoglobin, number of days of hospitalisation and blood count were recorded for both groups. RESULTS: Tranexamic acid significantly reduced the volume of blood loss during the surgery when compared with the control group (489.17± 106.7 mL vs 900.83 ± 113.7 mL). Considering the duration of operation and the treatment groups only, the mean total blood loss in the control group was 411.67 mL more than that in the tranexamic acid group. None of the patients of the TXA group required blood transfusion post-operatively. There was no difference in the length of hospital stay between the 2 groups. Two of the patients of the saline group required blood transfusion post-surgery due to significant drop in haemoglobin. The average drop in haemoglobin was 2 ± 1.4 in the tranexamic group and 4 ± 1.09 in the saline group. CONCLUSION: Pre-operative intravenous bolus administration of tranexamic acid at 10 mg/kg reduces blood loss compared with placebo during the surgery.

17.
J Clin Diagn Res ; 8(3): 225-8, 2014 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-24783143

RESUMO

BACKGROUND: The keratocystic odontogenic tumors is a benign but one of most aggressive developmental cyst with many distinguishing clinical and histologic features and high recurrence rate. In the given study, authors have studied and presented their experience of managing Keratocystic odontogenic tumour. The aim of the study was to define an appropriate treatment protocol for the management of KCOT. MATERIALS AND METHODS: Total 8 patients, whose histopathological reports confirmed Gorlin - Goltz syndrome and KCOT, with age between 10 - 50 years, were selected from cases being treated at Sree Balaji Dental College, Chennai, India. Enucleation and resection were the surgical techniques employed. Modality of treatment was based on parameters like age , size, aggressiveness and extent of the lesion. All the patients were operated under general anaesthesia. Cases were studied, reviewed and followed up for five years between 2007-2012. RESULTS: The study included 8 cases in which three cases were opted for resection and five cases for enucleation followed by application of Carnoy's solution. CONCLUSION: Treatment modality should be decided on age, extent, aggressiveness and nature of the tumour.

18.
ScientificWorldJournal ; 2014: 192512, 2014.
Artigo em Inglês | MEDLINE | ID: mdl-24723794

RESUMO

A reconfigurable hardware architecture for the implementation of integer wavelet transform (IWT) based adaptive random image steganography algorithm is proposed. The Haar-IWT was used to separate the subbands namely, LL, LH, HL, and HH, from 8 × 8 pixel blocks and the encrypted secret data is hidden in the LH, HL, and HH blocks using Moore and Hilbert space filling curve (SFC) scan patterns. Either Moore or Hilbert SFC was chosen for hiding the encrypted data in LH, HL, and HH coefficients, whichever produces the lowest mean square error (MSE) and the highest peak signal-to-noise ratio (PSNR). The fixated random walk's verdict of all blocks is registered which is nothing but the furtive key. Our system took 1.6 µs for embedding the data in coefficient blocks and consumed 34% of the logic elements, 22% of the dedicated logic register, and 2% of the embedded multiplier on Cyclone II field programmable gate array (FPGA).


Assuntos
Algoritmos , Segurança Computacional/instrumentação , Armazenamento e Recuperação da Informação/métodos , Processamento de Sinais Assistido por Computador/instrumentação , Análise de Ondaletas , Desenho de Equipamento , Análise de Falha de Equipamento
19.
Ann Maxillofac Surg ; 1(1): 42-7, 2011 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-23483114

RESUMO

BACKGROUND: A retrospective analysis of the 5-year survival rates of patients who underwent treatment for oral squamous cell carcinoma (OSCC) of the maxillary region was performed to analyse the prognostic factors for patient's survival. MATERIALS AND METHODS: Twenty-four patients with SCC of the maxillary region, who underwent treatment at our hospital between 1999 and 2009 were included in the study. The patients underwent primary surgical resection and elective bilateral neck dissection. The patients with tumor positive margins were referred for chemo-radiotherapy after surgery. RESULTS: The overall 5-year survival rate was 25%. The patients who had recurrence had presented with T3 or T4 lesions only. Of the patients who died, 14 out of the 18 were those who had tumor-positive margins and had undergone radiotherapy following surgery. CONCLUSIONS: Primary surgical treatment of SCCs of the maxillary region along with elective bilateral neck dissection yielded some improvement in survival rates, and can therefore be seen as a valuable strategy. Tumor-free resection margins and early detection of the lesion are the most important indicators for favorable prognosis.

20.
J Maxillofac Oral Surg ; 10(1): 38-44, 2011 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-22379319

RESUMO

AIM: The objective of this study was to compare the rate of complications encountered on using different incisions to access the fracture site for the open reduction and internal fixation of isolated subcondylar fractures. The parameters evaluated are: the occurrence of salivary fistula, infection, and injuries to the seventh facial nerve. An assessment of the surgical scar was also undertaken. MATERIALS AND METHODS: 20 patients who met the previous criteria and were willing to participate in the study were placed (five each) into the pre-auricular, submandibular, retromandibular transparotid or retromandibular transmassetric group based on the incision scar they selected after a description of the operation and being explained about the possible complications. RESULTS AND CONCLUSION: Comparison of the complications could not ascertain the superiority of any approach over the other since the outcomes were not statistically significant. However, judging by operator and assistants' subjective assessment, the retromandibular approaches seem to provide a more direct visual field and an almost straight line access for the fixation of the fracture. The transmassetric approach seems to be a safer approach since the nerves encountered can be visualized and avoided.

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